Integrated health data capture and analysis system

ABSTRACT

The present invention provides an integrated health care surveillance and monitoring system that provides real-time sampling, modeling, analysis, and recommended interventions. The system can be used to monitor infectious and chronic diseases. When faced with outbreak of an infectious disease agent, e.g., influenza virus, the system can identify active cases through pro-active sampling in high risk locations, such as schools or crowded commercial areas. The system can notify appropriate entities, e.g., local, regional and national governments, when an event is detected, thereby allowing for proactive management of a possible outbreak. The system also predicts the best response for deployment of scarce resources.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional Application No.61/253,015, filed Oct. 19, 2009, which application is incorporatedherein by reference in its entirety.

BACKGROUND OF THE INVENTION

An epidemic of infectious diseases capable of spreading across a largeregion, e.g., a continent or the entire world, can be hugely costly tosocieties. Such incidences include pandemics of influenza, smallpox,tuberculosis, human immune deficiency virus (HIV), and Severe AcuteRespiratory Syndrome (SARS). The World Bank estimated in 2008 that a flupandemic could cost $3 trillion and result in a nearly 5% drop in worldgross domestic product (GDP). The World Bank further estimated that morethan 70 million people could die worldwide in a severe pandemic. Othershave estimated that a flu pandemic could cause an economic recession inthe United States, costing the country at least $500 billion to $675billion in the near term. In 2003, SARS disrupted travel, trade and theworkplace in the Asia Pacific region and cost the region about $40billion. The SARS pandemic lasted for six months, killing at least 1000of the 8,000 people it infected in 25 countries. The city of Toronto,Calif. was closed to air traffic for several weeks and sufferedsignificant financial loss.

In 2009, the spring flu season cost billions of dollars even though itonly lasted only a few weeks. The 2009-2010 winter flu season isanticipated to start by late August and could run through April 2010.Even if working vaccines are available, their supplies are expected tobe limited and cannot be expected to stop the flu for several months.Economic losses can be minimized if the flu can be contained throughproactive screening that allows for effective anti-viral administrationand narrowly targeted quarantines.

Economic loss due to “avoidance behaviors” is even greater than the costof treating flu victims. The cost includes reducing air travel, avoidingtravel to infected destinations and reducing consumption of services,such as mass transit, dining out, shopping, etc. According to the WorldBank, if a flu epidemic approached the 2.5% mortality rates similar to1918-19 flu, avoidance behaviors would cost a region five times morethan losses from mortality or work absenteeism.

SUMMARY OF THE INVENTION

There is a pressing need for an infrastructure to mitigate the spread ofinfectious diseases such as influenza when it occurs. The presentinvention meets this need through an integrated system that providesreal-time sampling, modeling, analysis, and/or recommendedinterventions. The system can identify active cases in an outbreakthrough pro-active sampling in high risk locations, such as schools orcrowded commercial areas, and can allow for sampling and quarantine ofsurrounding cases to help eradicate the outbreak. The system can alsosuggest an appropriate response for deployment of scarce resources andpredict the impact of such mitigation both in terms of reduction ofmortality and morbidity and economic impact. Furthermore, the systems ofthe present invention can help the government provide accurate, morereliable, and timely information that may reduce unnecessary avoidancebehavior and save billions of dollars.

In one aspect, the present invention provides a system for modeling aprogression of a disease within a population, comprising: a staticdatabase component comprising static data related to the disease and/orthe population; a dynamic database component comprising dynamic dataabout the population and individual subjects; and a computer modelingcomponent that is configured to model the data in the static databasecomponent and the dynamic database component, thereby modeling thedisease within the population. The disease can be an infectious diseaseor a chronic disease.

In some embodiments, the infectious disease agent or an analyte thereofcomprises an adenovirus, Bordella pertussis, Chlamydia pneumoiea,Chlamydia trachomatis, Cholera Toxin, Cholera Toxin β, Campylobacterjejuni, Cytomegalovirus, Diptheria Toxin, Epstein-Barr NA, Epstein-BarrEA, Epstein-Barr VCA, Helicobacter Pylori, Hepatitis B virus (HBV) Core,Hepatitis B virus (HBV) Envelope, Hepatitis B virus (HBV) Surface (Ay),Hepatitis C virus (HCV) Core, Hepatitis C virus (HCV) NS3, Hepatitis Cvirus (HCV) NS4, Hepatitis C virus (HCV) NS5, Hepatitis A, Hepatitis D,Hepatitis E virus (HEV) orf2 3 KD, Hepatitis E virus (HEV) orf2 6 KD,Hepatitis E virus (HEV) orf3 3 KD, Human immunodeficiency virus (HIV)-1p24, Human immunodeficiency virus (HIV)-1 gp41, Human immunodeficiencyvirus (HIV)-1 gp120, Human papilloma virus (HPV), Herpes simplex virusHSV-1/2, Herpes simplex virus HSV-1 gD, Herpes simplex virus HSV-2 gG,Human T-cell leukemia virus (HTLV)-1/2, Influenza A, Influenza A H3N2,Influenza B, Leishmania donovani, Lyme disease, Mumps, M. pneumoniae, M.tuberculosis, Parainfluenza 1, Parainfluenza 2, Parainfluenza 3, PolioVirus, Respiratory syncytial virus (RSV), Rubella, Rubeola, StreptolysinO, Tetanus Toxin, T. pallidum 15 kd, T. pallidum p47, T. cruzi,Toxoplasma, or Varicella Zoster.

In other embodiments, the disease is an infectious disease comprising amicrorganism, a microbe, a virus, a bacterium, an archaeum, a protozoan,a protist, a fungus or a microscopic plant. The virus can compriseinfluenza or HIV. The bacterium can comprise mycobacterium tuberculosis.The protozoan can comprise malaria.

In still other embodiments, the disease is a chronic disease orcondition comprising diabetes, prediabetes, insulin resistance,metabolic disorder, obesity, or cardiovascular disease.

The static database component of the invention can include informationabout the individuals in the population. The information about theindividuals in the population can include one or more of age, race, sex,location, genetic factors, single nucleotide polymorphisms (SNPs),family history, disease history or therapeutic history.

The static database component can also comprise information about thedisease. The information about the disease can include one or more ofvirulence, contagiousness, mode of transmission, treatment availability,vaccine availability, death rate, recovery time, cost of treatment,infectivity, rate of spread, rate of mutation, and past outbreak.

In some embodiments, the data in the dynamic database component isupdated in real-time. In some embodiments, the data in the dynamicdatabase component comprises an indication of the disease state of theindividuals in the population. The indication of the disease state of anindividual can be determined by measuring a biomarker, a physiologicalparameter, or a combination thereof.

When the disease monitored by the invention is influenza, thebiomarker/s can include hemagglutinin and/or neuraminidase. Thehemagglutinin can be selected from the group consisting of H1, H2, H3,H4, H5, H6, H7, H8, H9, H10, H11, H12, H13, H14, H15, and H16, and theneuraminidase can be selected from the group consisting of N1, N2, N3,N4, and N5. In some embodiments, the hemagglutinin comprises H1 and theneuraminidase comprises N1. In some embodiments, the hemagglutinincomprises H5 and the neuraminidase comprises N1.

The biomarker measured by the invention can be a host antibody. Forexample, the biomarker can be an IgM antibody, an IgG antibody or an IgAantibody against a disease marker.

In some embodiments, the biomarker comprises a marker of inflammation.Such marker of inflammation can be a cytokine or C-reactive protein. Themarker of inflammation can also be IL-1β, IL-6, IL-8, IL-10, or TNFα.

In some embodiments, the biomarker is measured in a sample of bodilyfluid from the individual. Exemplary bodily fluids include withoutlimitation blood, plasma, serum, sputum, urine, feces, semen, mucous,lymph, saliva, or nasal lavage.

In some embodiments, the physiological parameter measured by theinvention comprises one or more of body weight, temperature, heart rate,blood pressure, mobility, hydration, ECG, or alcohol use.

The biomarker or physiological parameter can be determined using apoint-of-care device. The point of care devices can be deployedaccording to instructions determined by the computer modeling component.The point of care device can perform without limitation one or more ofcartridge assays, real time PCR, rapid antigen tests, viral culture, andimmunoassays. The point of care device can measure more than onebiomarker with more than 30% better accuracy and/or precision thanstandard methods. In some embodiments, the system comprises a pluralityof point of care devices. The point of care devices can be positioned atone or more of a school, a workplace, a shopping center, a communitycenter, a religious institution, a hospital, a health clinic, a mobileunit, or a home.

The point of care device can comprise a portable instrument. Forexample, the point of care device can include a portable cartridge. Insome embodiments, the cartridge is configured to accept reagents formeasuring the biomarkers. The biomarkers can be measured according to aprotocol communicated from the computer modeling component. In someembodiments, the cartridge is configured to measure a set of biomarkersfrom a plurality of bodily fluid samples.

The point of care device of the invention can include a graphical userinterface configured for data entry.

In some embodiments, the point of care device is configured tocommunicate the biomarker or physiological parameter measurements to thecomputer modeling component. The communication can include wirelesscommunication, wired communication, or a combination thereof. Wirelesscommunication comprises without limitation WiFi, Bluetooth, Zigbee,cellular, satellite, and/or WWAN. The communication can also beperformed over a secure internet communication. In some embodiments, thepoint of care device is configured to perform two way communicationswith the computer modeling component.

In some embodiments of the system of the invention, the modeling resultsare updated in real time when updated dynamic data becomes available,e.g., after the computer modeling component receives updated informationfrom a point of care device.

The computer modeling component can be configured to present themodeling results to one or more of healthcare professionals, governmentagencies and individual human subjects. The computer modeling componentcan also be configured to predict one or more courses of action based onthe modeling results. The one or more courses of action are rankedaccording to a ranking parameter, including without limitation rankingby financial considerations, number of affected individuals,quality-adjusted life year (QALY), and/or quality-adjusted life year(QALY) per economic cost unit.

The one or more courses of action comprise a strategy to control thespread of the disease. The strategy to control the spread of the diseasecan include one or more of household quarantine, individual quarantine,geographic quarantine, social distancing, hospitalization, schoolclosure, work place closure, travel restrictions, public transitclosure, therapeutic treatment or intervention, prophylactic treatmentor intervention, vaccination, provision of protective clothing,provision of masks, and additional point-of-care testing. The strategyto control the spread of the disease can further include one or more ofcounseling at risk or affected individuals for behavior modification,repeated biomarker and/or physiological measurements, and reward for theindividual. Still further, the strategy to control the spread of thedisease can include one or more of patient triage recommendations,resource management, efficacy index for each strategy, costs of eachstrategy, return on investment for each strategy. The strategy tocontrol the spread of the disease can be one or more of targetedprophylaxis, blanket prophylaxis, targeted antibiotic prophylaxis,blanket antibiotic prophylaxis, targeted anti-viral prophylaxis, blanketanti-viral prophylaxis, targeted vaccination, and blanket vaccination.The targeted prophylaxis or vaccination can be targeting the prophylaxisor vaccination to children between 1-4 yrs of age, children between 5-14yrs of age, pregnant women, young adults between 15-30 yrs of age,first-line medical response workers, individuals identified to at highrisk of mortality, or geriatric individuals.

In some embodiments of the invention, the computer modeling component isconfigured to estimate a surveillance strategy based on the modelingresults. The surveillance strategy can include determining the diseasestatus of an individual or group of individuals using a point of caredevice. The surveillance strategy can be updated when a diseasedindividual is detected. In some embodiments, the updated strategycomprises one or more of testing a household comprising the diseasedindividual, testing a school comprising the diseased individual, andtesting a work place comprising the diseased individual. The updatedstrategy can further be one or more of quarantine, prophylaxis orhospitalization.

In some embodiments, the computer modeling component comprises agraphical interface for displaying modeling results to a user.

The computer modeling component can include a plurality of nonlinearordinary differential equations, and/or a plurality of parameters. Insome embodiments, the computer modeling component comprises a learningmachine that updates the plurality of parameters when the static dataand/or dynamic data are updated.

The model of the data can be configured to include a plurality ofstates. In some embodiments, the plurality of states comprises one ormore of: susceptible individuals, early exposed individuals, lateexposed individuals, early infected individuals, late infectedindividuals, recovered individuals, individuals who died due to theinfection and/or associated complications, asymptomatic individuals,individuals given therapeutic treatment, individuals given therapeutictreatment and quarantined, individuals treated prophylactically,vaccinated individuals, individuals protected due to vaccination, earlyinfected individuals who are hospitalized, late infected individuals whoare hospitalized, susceptible individuals who are home quarantined,early exposed individuals who are home quarantined, late exposedindividuals who are home quarantined, early infected individuals who arehome quarantined, late infected individuals who are home quarantined,asymptomatic individuals who are home quarantined, susceptibleindividuals quarantined in the whole neighborhood, early exposedindividuals quarantined in the whole neighborhood, late exposedindividuals quarantined in the whole neighborhood, early infectedindividuals quarantined in the whole neighborhood, late infectedindividuals quarantined in the whole neighborhood, asymptomaticindividuals quarantined in the whole neighborhood, amount of therapeuticdrug doses available, amount of antivirals and/or antibiotics availableto the target population, home quarantined individuals that arevaccinated, home quarantined individuals that are protected due tovaccination, home quarantined individuals that recovered, susceptibleindividuals earmarked by mitigation policies for action, early exposedindividuals earmarked by mitigation policies for action, late exposedindividuals earmarked by mitigation policies for action, asymptomaticindividuals earmarked by mitigation policies for action, early infectedindividuals earmarked by mitigation policies for action, late infectedindividuals earmarked by mitigation policies for action,prophylactic-treated individuals earmarked by mitigation policies foraction, vaccinated individuals earmarked by mitigation policies foraction, protected individuals earmarked by mitigation policies foraction, recovered individuals earmarked by mitigation policies foraction, susceptible individuals earmarked for therapeutic treatment,early exposed individuals earmarked for therapeutic treatment, lateexposed individuals earmarked for therapeutic treatment, asymptomaticindividuals earmarked for therapeutic treatment, early infectedindividuals earmarked for therapeutic treatment, late infectedindividuals earmarked for therapeutic treatment, susceptible individualsearmarked for surveillance, early exposed individuals earmarked forsurveillance, late exposed individuals earmarked for surveillance,asymptomatic individuals earmarked for surveillance, early infectedindividuals earmarked for surveillance, late infected individualsearmarked for surveillance, prophylactic individuals earmarked forsurveillance, vaccinated individuals earmarked for surveillance,protected individuals earmarked for surveillance, susceptibleindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, early exposed individuals in whole neighborhoodquarantine earmarked by mitigation policies for action, late exposedindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, asymptomatic individuals in whole neighborhoodquarantine earmarked by mitigation policies for action, early infectedindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, late infected individuals in whole neighborhoodquarantine earmarked by mitigation policies for action,prophylactic-treated individuals in whole neighborhood quarantineindividuals earmarked by mitigation policies for action, cumulativenumber of therapeutic doses administered, cumulative number ofantivirals and/or antibiotics administered, cumulative number of homequarantined asymptomatic individuals, cumulative number of homequarantined symptomatic individuals, cumulative number of total infectedindividuals, cumulative number of infected individuals who are notquarantined, cumulative number of infected individuals with some actiontaken, cumulative number of hospitalized individuals, and cumulativenumber of deaths.

In another aspect, the present invention provides a system forcontrolling spread of influenza within a population, comprising: astatic database component comprising static data related to theinfluenza and/or the population; a dynamic database component comprisingdynamic data about the population; and a computer modeling componentthat is configured to model the data in the static database componentand the dynamic database component, thereby modeling the incidence ofthe influenza within the population.

In still another aspect, the present invention provides a system forcontrolling spread of human immunodeficiency virus (HIV) within apopulation, comprising: a static database component comprising staticdata related to the HIV and/or the population; a dynamic databasecomponent comprising dynamic data about the population; a computermodeling component that is configured to model the data in the staticdatabase component and the dynamic database component, thereby modelingthe incidence of the HIV within the population.

In yet another aspect, the present invention provides a system forcontrolling spread of hepatitis within a population, comprising: astatic database component comprising static data related to thehepatitis and/or the population; a dynamic database component comprisingdynamic data about the population; and a computer modeling componentthat is configured to model the data in the static database componentand the dynamic database component, thereby modeling the incidence ofthe hepatitis within the population.

In an aspect, the present invention provides a system for controllingspread of diabetes within a population, comprising: a static databasecomponent comprising static data related to the diabetes and/or thepopulation; a dynamic database component comprising dynamic data aboutthe population; and a computer modeling component that is configured tomodel the data in the static database component and the dynamic databasecomponent, thereby modeling the incidence of the diabetes within thepopulation.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 illustrates a simplified model representation.

FIG. 2 illustrates a model representation taking into account variousstates and transitions between states.

FIG. 3 illustrates an assay for H1N1 antigen using sandwich complexes infour different configurations.

FIG. 4A illustrates an assay for host anti-virus antibodies. The figureillustrates a spike recovery assay for host anti-H1N1 antibodies. Shownis a version using α-H1/α-N1 configuration. FIG. 4B illustrates directassays for α-H1N1 antibodies illustrating sandwich complexes.

FIG. 5 illustrates an exemplary device that can be used in the presentinvention. The exemplary devices comprise assay units, reagents unit,and other modular components.

FIG. 6 illustrates two side-cut away views of the exemplary device thatcan be used in the present invention. The exemplary device comprisescavities in the housing of the device shaped to accommodate an assayunit, a reagent unit, and a sample tip.

FIG. 7A demonstrates an exemplary assay unit that comprises a small tipor tubular formation. FIG. 7B demonstrates an example of a sample tip asdescribed herein.

FIGS. 8A and 8B illustrate two examples of a reagent unit comprising acup.

FIG. 9 illustrates a thin film, for example, contamination, within thetip when a liquid is expelled and another liquid aspirated.

FIG. 10 demonstrates an example of a system comprising a device and afluid transfer device.

FIG. 11 illustrates an exemplary system of the invention comprising aheating block for temperature control and a detector.

FIG. 12 demonstrates an exemplary a system wherein a patient deliversblood to a device and then the device is inserted into a reader.

FIG. 13 illustrates the process flow of building a system for assessingthe medical condition of a patient.

FIGS. 14A through 14E demonstrate an example of a plasma separationmethod wherein a whole blood sample has been aspirated into a sample tipand a magnetic reagent is mixed and suspended with the sample, then amagnetic field is applied to the whole blood sample and magnetic reagentmixture. Separated blood plasma sample can then be distributed into awell of a device.

FIG. 15 demonstrates an exemplary method of a control assay as describedherein comprising a known quantity of control analyte.

FIG. 16 illustrates an exemplary embodiment of a Health Shield userinterface.

FIG. 17 illustrates another exemplary embodiment of a Health Shield useinterface.

FIG. 18 illustrates simulation of the 2009 La Gloria outbreak with andwithout Health Shield mitigation policies.

FIG. 19 illustrates diabetes risk prediction visualization.

FIG. 20A illustrates the detection of H1N1 viral particles using a pointof care device. FIG. 20B illustrates the detection of H1N1 viralparticles using a point of care device in clinical samples.

FIG. 21 illustrates the detection of host antibodies using a point ofcare device.

FIG. 22A illustrates the detection of host antibodies using a point ofcare device. FIG. 22B illustrates the dynamic range of host antibodydetection using a point of care device.

FIG. 23 illustrates the detection of human cytokine IL-6 using a pointof care device.

FIG. 24 illustrates the detection of protein-C and C-reactive protein(CRP) using a point of care device in a patient undergoing chemotherapy.

FIG. 25 illustrates the detection of glucagon-like peptide 1 (GLP-1)using a point of care device.

FIG. 26 illustrates the detection of C-peptide, an insulin precursor,using a point of care device.

FIG. 27 illustrates the detection of C-peptide using a cartridge pointof care device compared to a reference detection system (Linco).

FIG. 28A illustrates the measurement of GLP-1 in three human subjectsafter feeding. FIG. 28B illustrates the measurement of C-peptide overthe course of the same experiment.

FIG. 29 illustrates a calibration curve correlating an assay unit and areagent unit for conducting an assay for VEGFR2.

FIG. 30 illustrates CRP concentration plotted against the assay signal(photon counts) and the data fitted to a 5-term polynomial function togenerate a calibration function.

FIG. 31 shows a fit was achieved between a model and the values of theparameters Smax, C0.5 and D as described herein.

FIG. 32 displays data according to the dilution used to achieve thefinal concentration in an assay tip.

FIG. 33 illustrates the normalized assay response (B/Bmax) is plottedagainst the log normalized concentration (C/C0.5) for relativedilutions: 1:1 (solid line), 5:1 (dashed line), and 25:1 (dotted line).

FIGS. 34 and 35 illustrate a similar example as FIG. 33 at differentnormalized concentrations.

FIG. 36A shows a spike in IL-6 in septic individuals. FIG. 36B shows adecline in Protein C in septic individuals.

FIG. 37 shows an increase in Il-6 and TNF-α (right panel) in anindividual as the load of H1N1 influenza increased in the patient (leftpanel).

DETAILED DESCRIPTION OF THE INVENTION

In one embodiment, the present invention provides an integrated healthdata capture, analysis and pandemic mitigation solution, referred toherein as the Health Shield (HS). HS can be used for infection caused bythe influenza virus and other pathogenic agents prone to endemic orpandemic spread. Flu outbreaks cost billions of dollars and cannotpresently be completely contained by vaccination. Economic losses can beminimized if the flu can be contained through proactive screening thatallows for administration of effective anti-viral agents and narrowlytargeted quarantines. Based on epidemic models, activating the HS of theinvention can reduce spread of the virus, e.g., by at least 50%, throughproactive sampling and containment. The HS can also reduce unnecessaryavoidance behavior by tracking the virus's spread in real time. Wheredesired, test results can be wirelessly relayed to a server operating HSsoftware. Accordingly, appropriate entities (e.g., local, regional andnational governments) can be notified with alerts when an event isdetected, thereby allowing for proactive management of a possibleoutbreak.

In further embodiments, the Health Shield infrastructure providesstrategic industrial and commercial parks as “safe zones,” which alloweconomically important activities to continue. As a result, fewerworkers will be infected with the virus, and schools and businesses willbe less disrupted. Pandemic mitigation strategies will maintainproductivity to drive economic growth and preclude actions prompted bypanic.

The system can comprise an integrated sampling and modeling technologysuite embedded in a real-time informatics infrastructure. The ability tosample, model, and learn from data as it is acquired longitudinally,enables the development of an optimal strategy for the care andmanagement of disease both on an individual and population basis. Customapplications can be built for numerous diseases and therapeutic areas.The HS infrastructure can also be used to protect a region from a widespectrum of threats beyond infectious disease, including chronic diseaseand bioterrorism threats.

I. Health Shield Infrastructure

The Health Shield provides a system to contain the spread of infectiousdiseases through integrated, automated, and real-time sampling,modeling, analysis, and recommended interventions. For example, the HScan identify active cases in an outbreak (through pro-active sampling inhigh risk locations, such as schools or crowded commercial areas) anddirect the sampling and defensive measures, e.g., quarantine, ofsurrounding cases to mitigate or eradicate the outbreak. HS algorithmscharacterize spread of the epidemic similarly to the case of a forestfire, where the THS models' mitigation policy aims to eradicate “hotspots” before a “fire” can take hold and spread and/or can create afire-break around a disease hot spot.

In some embodiments, the HS comprises two technological components—aField System (FS) and an Operating System (OS)—that can be adapted formanagement of chronic diseases to improve health outcomes and decreasehealthcare costs.

(a) Field System (FS)

The Field System components of the HS can be deployed at various pointsof care, including without limitation a clinic, a community site (e.g.,school, community center), a hospital, a doctor's office or anindividual's home. The FS can also use any number of platforms tomonitor disease, e.g., immunoassays, PCR assays, real-time PCR,microorganism plating, etc. The FS also includes standard medicalequipment, e.g., scales to determine weight, blood pressure devices,thermometers to measure temperature, ruler to measure height, etc. Insome embodiments, the FS devices comprise customized portable,single-use cartridges, as described herein. The FS collects relevantdata in the field, and transmits the data to the OS.

In some embodiments, the Field System comprises a measurement deviceintended to be deployed in an area to be monitored. In some embodiments,the FS analyzes bodily fluid samples, e.g., blood from a finger stick,in real-time. The system analyzes the bodily fluids for evidence ofinfection or disease by detecting, e.g., markers of a pathogen, nucleicacids, proteins, glycoproteins, lipids, or a combination thereofindicative of a disease condition. In some embodiments, the FSsimultaneously measures multiple markers including one or more ofselected antigens or the pathogen, antibodies directed to the pathogen,intracellular or cell surface proteins or glycoproteins, and cytokinesindicative of the response of an infected subject to a given pathogen,(e.g., a viral strain or other microorganism). The system can alsocollect environmental, demographic, personal and physiological (e.g.temperature, blood pressure) information. In some embodiments, suchinformation is collected through a graphical touchscreen interface.Individualized content can be analyzed by a remote system to facilitatemitigation strategies in real-time.

In some embodiments, the FS includes cartridges that perform assays onthe bodily fluids. The devices include without limitationnon-significant risk devices, and the assays can be validated underappropriate guidelines, e.g., those provided by the U.S. Federal DrugAdministration (FDA) and/or International Conference on Harmonization(ICH). Cartridges used by the present invention are described in U.S.patent application Ser. No. 11/389,409 entitled “POINT-OF-CARE-FLUIDICSYSTEMS AND USES THEREOF,” U.S. patent application Ser. No. 11/746,535entitled “REAL-TIME DETECTION OF INFLUENZA VIRUS,” and U.S. patentapplication Ser. No. 12/244,723 entitled “MODULAR POINT-OF-CARE DEVICES,SYSTEMS, AND USES THEREOF” and are described in further detail below.The measurement systems can be self-contained and few if any extramaterials are required to operate them. In some embodiments, the onlyrequirement for an FS system is a power source for the instruments. Inother embodiments, the power source is provided with the FS in form of abattery, generator, solar or other portable power source. The cartridgescan be pre-loaded with the desired assays and require little or nopreparation prior to use. For example, some or all assay components canbe stored in a refrigerator (e.g., at about 4 degrees C.) prior todeployment.

The FS platform can run any appropriate assay that is currentlyperformed in the conventional laboratory infrastructure. New assays canbe rapidly transferred and fully validated. In some embodiments, assaysthat are entirely new to the HS system can be customized and validatedwithin less than about three months, two months, one month, 3 weeks, 2weeks or less than about 1 week. In some embodiments, the assays run onHS Systems are validated under FDA ICH guidelines.

The Field Systems can be placed at any desired point of care, e.g., anarea suspected or known to be at risk of infection or disease. Point ofcare testing (POCT) is defined by a near-patient testing system.Exemplary points-of-care include but are not limited to the home,clinic, schools, or commercial centers. In some embodiments, the FS isdeployed in mobile units. Thus, it should be understood that medicalexperts are not necessarily required for the testing. To enable this,the FS can be engineered to be simple to use and provides all directionsfor use in a simple user interface with a touch screen. In someembodiments, the systems are designed for non-computer literateindividuals to test themselves in their own homes. In such a setting,the data can be sent to a remote system, e.g., the Operating System asdescribed below, where officials or others monitoring the assays canlearn of positive test results. In some embodiments, the testing anddata upload/analysis are performed in real-time so that containmentmeasures can be initiated immediately.

In some embodiments, the systems are deployed in public locations. Ifdesired, standard public health employees can be trained to do thetesting. In some embodiments, the systems are designed so that totaltraining time is minimized at a given site. For example, currentdeployment demonstrates that training should require no longer than halfan hour per site, although supplemental and advanced training can beperformed as appropriate. In some embodiments, trained individuals canin turn train others on using the systems. The FS can be successfullyused in the home by patients who have no medical training—as the testingis designed to be fully automated and uses a graphical touch-screeninterface on the instrument to walk users through the test process. Insome embodiments, the only steps required from a user are to: 1) place asample into the cartridge, e.g., sputum or a finger-stick which can beperformed by the user themselves using a disposable single-use lancetjust as used in diabetes management for glucose monitoring; and 2)insert the cartridge into the accompanying instrument, as described inmore detail below.

Non-limiting customized cartridge devices for use with the FS of theinvention are described in U.S. patent application Ser. No. 11/389,409entitled “POINT-OF-CARE-FLUIDIC SYSTEMS AND USES THEREOF,” U.S. patentapplication Ser. No. 11/746,535 entitled “REAL-TIME DETECTION OFINFLUENZA VIRUS,” and U.S. patent application Ser. No. 12/244,723entitled “MODULAR POINT-OF-CARE DEVICES, SYSTEMS, AND USES THEREOF.”Such devices are further detailed below.

(b) Operating System (OS)

The data collected from each FS device can be securely transmitted tothe Operating System in real-time through network connection, e.g., overa broadband, wireless, satellite or cellular network. One of skill inthe art will appreciate that network communications often comprisemultiple hops, e.g., an FS device can connect to a wireless local areanetwork (WLAN) that is securely connected to the World Wide Web throughbroadband landlines.

In some embodiments, the Operating System includes one or more serversas are known in the art and commercially available. Such servers canprovide load balancing, task management, and backup capacity in theevent of failure of one or more of the servers or other components ofthe system, to improve the availability of the OS. A server can also beimplemented on a distributed network of storage and processor units, asknown in the art, wherein the data processing according to the presentinvention reside on workstations such as computers. A server of the OScomponent can include a database and system processor. A database canreside within the server, or it can reside on another server system thatis accessible to the server. As the information in a database maycontains sensitive information, a security system can be implementedthat prevents unauthorized users from gaining access to the database.

In some embodiments, the Operating System comprises a data engine thatimports data from a desired source to provide direction for epidemic orpandemic mitigation. The OS can translate the source data into astandardized format to be analyzed. In some embodiments, the data engineis self-learning and dynamically models a plurality of integrated datasets in real-time. This OS modeling approach provides several benefits.For example, the models can be trained to perform a variety ofcalculations, including but not limited to: 1) predicting outcomes forindividuals and populations; 2) considering the efficacy of proposedintervention strategies for individuals and populations; and 2)quantifying the socioeconomic effect of the recommended interventions.In some embodiments, the OS is made available to remote users via aremote interface. For example, the users can access the OS through asecure online web-portal or the like.

The OS software portal incorporates automatic modeling in a system thatis constantly learning from each new data point that is transmitted tothe software portal. The system thereby becomes increasingly morepredictive over time. In some embodiments, Monte Carlo modelingapproaches are used. Monte Carlo approaches rely on repeated randomsampling to compute results. Monte Carlo simulation considers randomsampling of probability distribution functions as model inputs toproduce hundreds or thousands of possible outcomes instead of a fewdiscrete scenarios. The results provide probabilities of differentoutcomes occurring. In some embodiments, the solution andrefitting/refining of model parameters sets is achieved by using reversesearch and integrated parameter estimation techniques. See, e.g.,Sheela, 1979-COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 19(1979) 99-106; Moles, et al. 2003-Genome Res. 2003 13: 2467-2474;Rodriguez-Fernandez, et al. BMC Bioinformatics 2006, 7:483-500;Barthelmann, et al. 2000—Advances in Computational Mathematics 12:273-288.

There is a rich literature surrounding the modeling and simulation ofepidemiological data. The basis of the McKendrick model is a stochasticprocess (Birth Process) that yields a series of differential equationsthat can be parameterized, explored, and, eventually, optimizedregarding the control and spread of the disease. A reasonablystraightforward analysis of the process is given by Chiang, C. L. 1978.An Introduction to Stochastic Processes and Their Applications. RobertE. Kreiger Publishing Co, Inc. Huntington, N.Y. p 517. Once the processis established in a stochastic space, and appropriately parameterized,explicit expressions for population moments and or extinctionprobabilities can be derived. If the process is straightforward theseexpressions can be modeled and estimated either in closed form ornumerically.

If the populations are large enough that stochastic variation is smallcompared to overall population sizes and system dynamics one can modelthe spread and growth of a disease state using differential equationssystems. For example, a simple SIR model (Susceptible, Infected,Removed) of SARS was explored by Choi and Pak, J Epidemiol CommunityHealth. 2003 October; 57(10):831-5. More complex models accounting forexposure, the SEIR model, have been explored by d'Onofrio, MathematicalBiosciences 179 (2002) 57-72, especially with respect to theoptimization of vaccination strategies. For influenza in particular,Stilianakis, et al., J Infect Dis. 1998 April; 177(4):863-73, looked atparticular aspects of drug resistance in the growth and spread ofdisease. Other aspects of disease modeling including spread anddiffusion kinetics (FitzGibbon, et al., MATHEMATICAL BIOSCIENCES128:131-155 (1995)), mathematical and numerical stability (Dwyer, etal., The American Naturalist, 150(6): 685-707; Inaba, J. Math. Biol.(1990) 28:411-434).

Simulation is a valuable tool in the solution of these complex systems.There are many models that lend themselves to simulation solution. See,e.g., Longini, et al., 1984, Int J. Epidemiology. 13:496-501; O'Neill,2002. A Tutorial Introduction to Bayesian Inference for StochasticModels Using Markov Chain Monte Carlo Methods. Math Biosci. 180:103-114;Gibson, G. J. 1997. Investigating mechanisms of Spatiotemporal EpidemicSpread Using Stochastic Models. Am Phytopathological Society.87:139-146. In particular, see Timpka, et al. (2005) AMIA 2005 SymposiumProceedings. 729-733, with regards to simulating influenza. In someembodiments, the model of epidemic growth and spread and the incumbentscreening and containment strategies are embedded into a healtheconomics model of cost effectiveness. See, e.g., Brandeau, et al.Journal of Health Economics 22 (2003) 575-598.

A simplified exemplary model representation according to the inventionis shown in FIG. 1. The model can be configured to describe the spread,surveillance, and mitigation with its attendant cost effectiveness forepidemic/pandemic policy management. Briefly, an at risk population issegmented into various states or conditions (represented by the circlesin the Figure), with flux components between each state modified by avariety of configurable parameters, including but not limited to therate of infection, the means and granularity of the surveillancemechanism, and the policy decision at hand. To aid the policy maker inthe decision process, both the out-of-pocket and societal costs, e.g.,QALYs, can be calculated by the model and displayed to the policy maker.

The model illustrated in FIG. 1 comprises a system of deterministicnonlinear ordinary differential equations. Each node (or state)represents a population of individuals having similar phenotypic anddisease characteristics, such as their state of infectiousness. Variousstates can also represent individuals in different locations, such as inschools, workplaces, during hospitalization, isolated quarantine, orhome isolation. A plurality of age groups, e.g., 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50 or more age groups,are represented by modular structure, thus allowing specification ofage-specific characteristics. In some embodiments, the model takes ageinto account in a continuum as opposed to within discrete groups. Thearrows shown connecting the nodes in the figure indicate flux from onestate to another. As described herein, the model parameters come from avariety of sources, e.g., literature reports, patient data, prioroutbreaks, and can be estimated based on data as desired. The modelprojections capture a range of possibilities based on the quantifieduncertainties. As the model predictions are implemented, the parameterscan be continuously adjusted in real time according to the actualresults in the field. For example, the effectiveness of variousmitigation policies might be reassessed and adjusted given real worldresults applied to the current, specific affected populations.

Those skilled in the art will appreciate that the model shown in FIG. 1can be expanded to take into account any number of relevant states andparameters. FIG. 2 shows a larger model representation. Each circlerepresents a class of individual and each arrow represents a transitionfrom one stated to another. Transitions from one state to another cantake into account changes from natural causes, or from interventions,e.g., therapeutic treatment. The model can also take into accounttransitions that don't involve disease state, e.g., change of socialinteraction with various groups. For example, a quarantined individualmay transition from community involvement to involvement with a limitednumber of individuals, e.g., contact being limited to health careworkers or other care-takers. The model parameters at the outset of anepidemic can be derived from data from the closest applicable previousdisease outbreak with the closest demographics and type of location(e.g., a city, a rural area). The model can be continuously refined byapplication of data gathered within the present epidemic to becomeprogressively better.

Near the top of FIG. 2, a flux from left to right is highlighted by therow P_(i), S_(i), E1_(i), E2_(i), I1_(i), I2_(i), R_(i), and D_(i).These states represent a disease spread model comprising states ofprophylactically treated, e.g., with anti-virals (P_(i)), susceptibleindividuals (S_(i)), early exposed individuals (E1_(i)), late exposedindividuals (E2_(i)), early symptomatic infected individuals (I1_(i)),late symptomatic infected individuals (I2_(i)), recovered—and thuspotentially immune—individuals (R_(i)), and the deceased (D_(i)). Anindividual can transition from state E2_(i) to state A_(i), whichrepresents the asymptomatic infectious subpopulation in the community athand. An individual can also transition to state V_(i), which representsvaccination. From the vaccinated state, an individual can transition toeither a cleared and immune state, C_(i), or to the ineffective andexposed state, E1_(i). By taking into account any number of individuals,i, the model can capture a population representation of epidemic spread.The delay criteria, E2_(i) and I2_(i), accommodate the time dependentspread of the disease. The segment above the disease spread modelrepresents the impact of a policy of treatment and its effects onpopulation wellness and disease spread, while the segment below thedisease specific spread represents a mitigation strategy of quarantine.The model integrates an active, user-defined surveillance strategy anduser defined mitigation strategy with a cost effectiveness matrix to aidin decision making. In some embodiments, the model accounts forsub-optimal disease mitigation. For example, even when a developingdisease hot spot has been located, there can be logistic delays ingetting therapeutic agents to the area and in implementing quarantine.These delays can permit further progression of the epidemic withoutmitigation. The model can take such sub-optimal mitigation into account.

The model equations form an Ordinary Differential Equation System (ODEs)with appropriately parameterized flux coefficients as defined by thearrows in FIG. 2. The basic form of the model is given by the vectorODE:dX/dt=f(X,t)

where X is a dimensionalized vector and the function f(X,t) isrepresented by a matrix of mixing parameters and functional interactionsas defined in the Figure. In the model in the figure, there are morethan 80 dimensions to the dimensionalized vector. One of skilled in theart will appreciate that the format and components of the matrix for thefunction f is derivable from FIG. 2 and the explanation herein.

The equation sets represented above are duplicated for each of a varietyof age groups, as described herein. Consider an example with seven agegroups. In the example, the conglomerate model of seven sets isreplicated for each geopolitical region in a given geographical region.The model then can be generalized to account for more wide spread of thedisease in a larger region. For example, by parameterizing the mixingmatrices and resource/cost tables, one can account for interregionaltravel and nationwide surveillance and mitigation strategies.

A variety of states modeled by the OS and presented in FIGS. 1 and 2 areshown in Table 1:

TABLE 1 Description and nomenclature for the states used to describe theoutbreak Variable Name Description S Susceptible individuals E1 Earlyexposed individuals E2 Late exposed individuals I1 Early infectedindividuals I2 Late infected individuals R Recovered individuals DIndividuals who have died due to the infection and associatedcomplications A Asymptomatic individuals T Individuals treated withantivirals Tq Individuals treated with antivirals & quarantined PIndividuals prophylaxtically treated with antivirals V Vaccinatedindividuals C Individuals protected due to vaccination H1 Early infectedindividuals who are hospitalized H2 Late infected individuals who arehospitalized QS Susceptible individuals who are home quarantined QE1Early exposed individuals who are home quarantined QE2 Late exposedindividuals who are home quarantined QI1 Early infected individuals whoare home quarantined QI2 Late infected individuals who are homequarantined QA Asyptomatics who are home quarantined QS_iso Susceptiblesquarantined in the whole neighborhood QE1_iso Early exposed individualsquarantined in the whole neighborhood QE2_iso Late exposed individualsquarantined in the whole neighborhood QI1_iso Early infected individualsquarantined in the whole neighborhood QI2_iso Late infected individualsquarantined in the whole neighborhood QA_iso Asymptomatics quarantinedin the whole neighborhood Dv Amount of drug doses available Da Amount ofantivirals available Q1v Home quarantined individuals that arevaccinated Q1c Home quarantined individuals that are protected due tovaccination Qr Home quarantined individuals that recovered SmSusceptibles earmarked by mitigation policies for action E1m Earlyexposed individuals earmarked by mitigation policies for action E2m Lateexposed individuals earmarked by mitigation policies for action AmAsymptomatics earmarked by mitigation policies for action I1m Earlyinfected individuals earmarked by mitigation policies for action I2mLate infected individuals earmarked by mitigation policies for action PmProphylactic-treated individuals earmarked by mitigation policies foraction Vm Vaccinated individuals earmarked by mitigation policies foraction Cm Protected individuals earmarked by mitigation policies foraction Rm Recovered individuals earmarked by mitigation policies foraction St Susceptibles earmarked for treatment with antivirals E1t Earlyexposed individuals earmarked for treatment with antivirals E2t Lateexposed individuals earmarked for treatment with antivirals AtAsymptomatics earmarked for treatment with antivirals I1t Early infectedindividuals earmarked for treatment with antivirals I2t Late infectedindividuals earmarked for treatment with antivirals Ss Susceptiblesearmarked for surveillance E1s Early exposed individuals earmarked forsurveillance E2s Late exposed individuals earmarked for surveillance AsAsymptomatics earmarked for surveillance I1s Early infected individualsearmarked for surveillance I2s Late infected individuals earmarked forsurveillance Ps Prophylactic individuals earmarked for surveillance VsVaccinated individuals earmarked for surveillance Cs Protectedindividuals earmarked for surveillance Sm_iso Susceptibles in wholeneighborhood quarantine earmarked by mitigation policies for actionE1m_iso Early exposed individuals in whole neighborhood quarantineearmarked by mitigation policies for action E2m_iso Late exposedindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action Am_iso Asymptomatics in whole neighborhoodquarantine earmarked by mitigation policies for action I1m_iso Earlyinfected individuals in whole neighborhood quarantine earmarked bymitigation policies for action I2m_iso Late infected individuals inwhole neighborhood quarantine earmarked by mitigation policies foraction Pm_iso Prophylactic-treated individuals in whole neighborhoodquarantine individuals earmarked by mitigation policies for action N_DvCumulative number of Drug doses administered N_Da Cumulative number ofAntivirals administered N_QA Cumulative number of home quarantinedasymptomatics N_QS Cumulative number of home quarantined symptomaticsN_TI Cumultative number of total infected individuals N_I Cumulativenumber of Infected individuals who are not quarantined N_Idet Cumulativenumber of Infected individuals with some action taken N_H Cumulativenumber of hospitalized individuals N_D Cumulative number of deaths

The model of the invention can be configured to take into account manycharacteristics of the individuals, populations and disease beingmonitored. In some embodiment, the force of infection is taken intoaccount in the model. The force of infection, also termed thetransmission rate, refers to the rate at which existing infectiousindividuals transmit the disease to susceptible individuals. In someembodiments, each infectious individual is given two attributes: anage-group j, based on the individual's age, and a mixing group k, basedon the individual's mixing pattern in the society. Mixing patternsinclude without limitations mixing freely with others in society, e.g.,at school or work, reduced mixing from taking days-off from work due toillness, etc. The force of infection exerted on population age-group iby all populations of age-groups j can be computed as follows:

$\Lambda_{i} = {\rho_{i}\beta{\sum\limits_{k}{\sum\limits_{j}{\varphi_{j}\left( {{\Delta_{ij}^{k}\theta\frac{I_{j}^{k}}{N_{j}^{k}}} + {\left( {1 - \theta} \right)\frac{I_{j}^{k}}{N_{t}}}} \right)}}}}$where,

-   -   β is rate of transmission (per day per infectious individual per        susceptible individual)    -   θ is parameter defining randomness of mixing between different        age-groups: if θ=1 the interactions are perfectly assortative,        if θ=0, the interactions are perfectly random    -   ρ_(i) is relative susceptibility of individuals in age group i    -   φ_(j) is relative infectiousness of infectious individuals of        age group j    -   Δ_(ij) ^(k) is a weight factor that accounts for the differences        in the relative extent of potentially transmission-causing        interactions between individuals of age-group i and those of        age-groups j and mixing-groups k    -   I_(j) ^(k) is the number of infectious individuals of age-group        j    -   N_(j) ^(k) is total number of individuals of age-group j and        mixing group k in the population    -   N_(t) is total number of individuals of all-age-groups in the        population

In the force of infection equation, the interaction weights Δ_(ij) ^(k)are calculated based upon

-   -   1. the time spent by an individual of age-group i in company of        individuals of age-group j and mixing-group k in different        locations such as work, school, home etc    -   2. the number of individuals of age-group j and mixing-group k        that come in potentially transmission-causing contact with an        individual of age-group i

Of the above parameters, ρ_(j), φ_(j), Δ_(ij) ^(k), I_(j) ^(k), N_(j)^(k) can change dynamically with time as a result of evolution of theepidemic, imposition of mitigation policies or both.

The OS model can include a number of mitigation policies that directmedical decision making policy when faced with an outbreak. Thesepolicies can be modeled for each particular setting, e.g., geographicallocation and disease or infectious agent, to best take advantage of theavailable resources. Each policy can be imposed with a realisticefficacy/compliance which can be estimated from historical data. Themodel can predict the results of implementing various mitigationpolicies, thereby providing the appropriate individuals with a suggestedresponse. Exemplary non-limiting mitigation policies are listed in Table2:

TABLE 2 Mitigation Policies Represented in the Model Community/Public 1.Individual hygiene: hand sanitizer, face masks, etc; Health Measures 2.Social distancing; 3. Hospital hygiene; 4. School/daycare closure; 5.Workplace closure; 6. Public transportation closure; 7. Householdquarantine; 8. Geographical area quarantine: e.g., neighborhood,village, town, city; 9. Individual quarantine; or 10. Travelrestrictions Pharmaceutical 1. Targeted prophylaxis, e.g., anti-viralProphylaxis (a) Household of an infected individual; (b) Workplace of aninfected individual; (c) Condition-targeted: individuals with primaryconditions; or (d) Health care workers treating infected individuals 2.Blanket prophylaxis, e.g., anti-viral; 3. Targeted vaccination: singleor multiple doses: (a) Children between 1-4 years of age; (b) Childrenbetween 5-14 years of age; (c) Pregnant women; (d) Young adults between15-30 years of age; (e) First-line medical response personnel; (f)Individuals identified at high risk of mortality; (g) Geriatrics; or (h)Middle aged individuals between 30-60 years of age. or 4. Blanketvaccination: single or multiple doses Treatment 1. Therapeuticadministration, e.g., anti-viral; 2. Hospitalization (antibiotic,anti-pyretic, saline, etc); or 3. Antibiotics treatment of quarantinedindividuals

In addition to mitigation policies, the OS model can incorporate resultsobtained in the field when performing surveillance with a variety ofdifferent technologies. These include the cartridge systems describedherein, rapid antigen test, immunofluorescence, immunoassays, real timePCR, viral culture test, physiological measures, urine and blood workup,etc. The model includes the representation of the sensitivity andspecificity of each test for samples from both asymptomatic individualsand symptomatic individuals. In addition, the turn around time for thedifferent tests can be included in the model.

Depending on each particular system, various forms of surveillancestrategies can be included in the model. In one embodiment, surveillancecomprises the testing of individuals reporting for testing voluntarily.The surveillance can also be performed for population-groups whichinclude, but are not limited to, the following:

-   -   Children between 1-4 yrs of age    -   Children between 5-14 yrs of age    -   Pregnant women    -   Young-adults between 15-30 yrs of age    -   First-line medical response workers    -   Individuals identified to at high risk of mortality    -   Geriatrics    -   Middle-aged individuals between 30-60 yrs of age

Each of these population-groups can be tested using any of the testingmethods or combinations thereof. Different proportions of asymptomaticindividuals and symptomatic individuals reporting for voluntary testingcan also be accounted for in the model.

In another embodiment, surveillance includes the testing based onimplementation of any surveillance policy as defined by the end user.The catalog of surveillance policies captured by the model includeswithout limitation the following:

-   -   Household surveillance: testing of entire household based on        index confirmed case    -   School surveillance: testing of school children based on index        confirmed case    -   Work place surveillance: testing of employees based on index        confirmed case        For confirmed cases indentified as a result of the surveillance        tests, appropriate action of quarantine, prophylaxis or        hospitalization can be taken.

In some embodiments, the HS allows for an automated analysis to beperformed using these methodologies for the selection, parameterization,and/or exploration of an appropriate epidemic model to implement theoptimal screening and containment strategy. The model can be modifiedaccording to a cost effectiveness health economics model. In someembodiments, the model is configured to predict spread of an infectiouspathogen in a heterogeneous human population. The models can take intoaccount regional demographics and individual risk factors. As describedin more detail below, in one embodiment, the model enables evaluation ofhealthcare mitigation policies, including without limitation: a)surveillance/testing strategies; b) hospitalization, home isolation, andquarantine policies; c) prophylactic vaccination and treatment policies,e.g., anti-viral therapy; and d) social distancing measures such asschool and workplace closures.

In addition to infectious outbreak dynamics, the model can provide costassessment as well as evaluation of the quality adjusted life years(QALY) saved by comparing alternative mitigation approaches. The modelcan be configured to take into account non-economic cost measures. Themodel can be configured to adjust for the cost associated with differenterrors, based on economic cost, temporal costs, or other factors, inorder to minimize the cost of the errors made by a model. For example,the model may assign a high cost to misdiagnosing an infected individualso that mitigation strategies are not put into place. The model couldthen adjust to favor avoidance of such errors. Similarly, a misdiagnosisfor a chronic condition may have a lesser cost as the individual may betested again before the disease has progressed very far. In the case ofan epidemic, predictions may not only relate to an individual's case,but to populations of people in different regions. Based on large setsof demographic data, the HS analytic system can be configured to predictrisk and costs optimized for both treatment and assay delivery. Forexample, locations with lower expected risk may be sampled less thanlocations with greater expected risk.

The OS has actions built in that are triggered when certain events aredetected. For example, alerts can be sent to government officials whenan infected individual is detected. Rules can be set to notify aclinician automatically by phone, email or fax when a case is detected.The detected individual and contacts, e.g., family members, co-workers,or anyone who has had contact with the individual in the past few days,weeks, months, or years, can also be notified. The rules that triggerthe action can be customized prior to deployment or during a period ofmonitoring depending on the needs of the situation.

The OS models also perform sanity and outlier checks on the datareceived from the FS. In some embodiments, actions are taken whenvariability or noise is identified in the data. In some embodiments, anassay for an individual is repeated when outliers are detected.

In some embodiments, the OS models can predict outcomes for individualsand populations. In some embodiments, the models match predictions—suchas response to infection, optimal treatment regimen for an individual orpopulation, and projected spread of the virus—to actual historical data,e.g., data from the spring flu season. In some embodiments, the modelsconsider the efficacy of proposed intervention strategies forindividuals and populations, including use of pre-emptive antiviraltherapies, reactive anti-viral therapies, quarantine, hospitalization,targeted closures and establishment of “safe zones” in key hotels,restaurants, schools, manufacturing plants and other locations. Themodels can also quantify the socioeconomic effect (in out-of-pocketexpenditures, lives saved, lost days of productivity, etc.) that therecommended interventions would have had at the time of each case.

In some embodiments, the Field Systems and OS are also customized toprovide solutions for various settings wherein the systems can improveoutcomes and reduce the cost of care. For example, the FS and OS canprovide health monitoring solutions for pharmaceutical and biotechnologycompanies and for consumers.

II. Deployment of the Health Shield

In some embodiments, the Health Shield comprises a fully integrateddiagnostic/Patient Health Record/Electronic Medical Record platform. Thedeployed Field System devices can be configured to be portable, and thuscan be deployed in a variety of points-of-care, including withoutlimitation a clinic, a community site (e.g., school, community center),a hospital, a doctor's office or an individual's home. As describedherein, portable FS devices can be configured to wirelessly connect to anetwork, requiring only an optional cable for power. In someembodiments, the network connection is made to a web-portal where assaydata is sent in real-time. The FS systems can be deployed in urbanenvironments near care centers and the same devices can by deployed inremote settings, e.g., even where patients live long distances from thenearest medical clinics.

The performance of the FS assays will vary from assay to assay but alltests are developed with a goal of high accuracy, e.g., via highspecificity and sensitivity. In some embodiments, the specificity isgreater than about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%,92%, 93%, 94%, 95%, 96%, 97%, 98% or greater than about 99%. In someembodiments, the specificity approaches 100%. In some embodiments, thesensitivity is greater than about 50%, 55%, 60%, 65%, 70%, 75%, 80%,85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or greater than about99%. In some embodiments, the sensitivity approaches 100%. The exactperformance of an assay can depend on a number of factors, including butnot limited to the performance of the marker being detected, the skillof the user and assay performance inherent in the device. In someembodiments, the FS systems are designed to be highly user friendly andrequire minimal skill to effectively operate. The time required forassay performance will also vary based on the use case for deployment.Each system is fully customized to best achieve the goals of deploymentso all specifications are set accordingly. In some embodiments, theassays are run in a matter of minutes, e.g., less than about 30 min, 25min, 20 min, 15 min, 10 min, 9 min, 8 min, 7 min, 6 min, 5 min, 4 min, 3min, 2 min, or less than about 1 minute. In some embodiments, the HSout-performs current centralized laboratory test analyses across broadranges of tests.

The assays of the present invention advantageously can examine a set ofmarkers. In some embodiments, the assays will measure both antibodiesand viral load to provide enhanced evaluation of the status of anindividual subject. The assays can also be designed to measure othermarkers for infection and response to infection, e.g., cytokineproduction levels, and will therefore provide additional informationabout the severity of illness, suggest individualized treatments, andcan also indicate when confirmatory tests are appropriate for a negativeinitial screen.

The system can also be configured to detect infection with mutant orother strains that are as yet uncharacterized. Before those strains areidentified, spikes in inflammatory markers can indicate that anindividual is infected with a strain that has not yet been identified,thereby allowing for potential rapid containment and identification ofthe fact the virus is mutating. Defensive measures (such as investmentsin vaccinations) can then be updated accordingly.

The HS technology is configurable to be simple to use and eliminates themultiple steps for data sampling analysis that would otherwise occurunder existing situations (e.g., sample collection, shipping, remoteanalysis, decision making). As a result, the HS can provide greateraccuracy and faster decision by providing real-time field data to acentral monitoring site, e.g., that of a governmental agency. The systemthereby provides the opportunity for optimal healthcare support anddirection. For example, the FS systems can be located at communityfriendly sites, such as pharmacies, schools, clinics, or recreationcenters, so that citizens could easily be tested and/or treated on adesirable basis, e.g., to monitor infectious diseases such as flu. Inaddition, because the device can be portable, community workers canvisit the elderly and others incapable of traveling, or make home visitswhen infection, e.g., by flu, is suspected. In some embodiments, thedata collected is analyzed on both an individual and population basedcircumstance. This assay data collected by the deployed FS devices canbe made available to providers, government officials, hospitals, or thelike.

When deployed in a region of interest, e.g., a school, community center,commercial center, locally, regionally, or nationally, the HS can beused to develop safety systems for monitoring potential adverse eventsand healthcare pandemics. The FS device can also be used in highscreening strategies where a large number of individuals, e.g., everyoneat-risk or suspected to be at-risk, can be tested on a routine basis ina preventive manner or in reaction to an outbreak. The data collected bythe FS is accumulated at the OS, which then aggregates and manages thecollective data. In some embodiments, the system requires only a smallsample of bodily fluid, e.g., a finger stick of blood, saliva or sputum,typical safety issues that arise from blood draws are greatly reduced oreliminated. In some embodiments, the real time data is used to helpselect the optimal biomarker assays for a given situation. In someembodiments, the analyte set is chosen prospectively as a sub-set from alarge assay menu. Thus, the ideal assay set appropriate for the earlystage of an epidemic (which might emphasize antigen detection) can bechanged later in the epidemic, e.g., to look for antibodies that provideinformation as to the likely stage of community immunity that may berelevant to management of subsequent epidemics.

When monitoring infectious disease, the Health Shield deploymentstrategy can provide screening and sampling for the at-risk populationderived from the minimum number of expected initial outbreaks. In someembodiments, the system assumes the same range of cases that hadoccurred to provide real world empirical data for modeling diseasespread.

An index case can potentially infect any number of secondaryindividuals. The number of secondary individuals can depend on anynumber of factors of the index case, including but not limited to age,mobility, living situation, work environment, socialization, andgeographical location. The HS can model these factors and others toestimate the potential spread of a given outbreak. In a non-limitingexample, real world data suggests that a typical index case is likely toinfect 50 other individuals. An exemplary infection pattern may comprise4 or 5 family members and 45 or 46 co-workers, friends, and other peoplewith whom the infected person has come in contact. In the HS rapidresponse model, each index case would require 25 to 50 secondary screens(regardless of age group) to prevent the people in contact with theindex case from becoming infected and spreading the virus. Depending oncharacteristics of the index case and infective agent, 5, 10, 15, 20,25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 or 100secondary screens might be required. In some embodiments, more than 100secondary screens may be necessary for an index case.

In some embodiments, the HS is equipped with an initial quantity of FSdevice cartridges, e.g., about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30,40, 50, 60, 70, 80, 90, or 100 times the expected number of index cases.In some embodiments, the system provides about 50 times the cartridgesper expected number of index cases. Each cartridge can be used to test abodily fluid sample, as described herein. The abundance of cartridgesprovides on-demand, proactive containment for pandemic mitigation. Oncethe infrastructure is activated, the HS provides additional on-demandshipments as required. This scheme provides screening and samplingsufficient to cover the at-risk population surrounding the index cases.

Individuals may be provided with a device when procuring a prescriptionof drugs by any common methods, for example, at a pharmacy. Theindividual may be given a device in a school, a work place, or otherarea of interest. The devices may also be distributed manually byhealthcare workers. When the device is distributed to an individual, theindividual's contact information, including without limitation cellphone, email address, text messaging address, or other means of wirelesscommunication, may at that time be entered into the databases of the OScomponent and associated with the individual therein. The OS system mayinclude a script or other program that can detect when a signalgenerated from a detection device has not yet been sent to the OSsystem, for example at a given time, and the OS system can then send analert notifying the individual to test a bodily fluid sample.

Because of the portability and size of the FS components of the HealthShield, the HS can become a part of everyday lifestyle for managingdisease and potential health hazards. In some embodiments, the systemsare placed in homes and at easily available locations. The real-timedata collection and data analysis provide a rapid pro-active healthcaresystem to respond to sudden outbreaks.

The HS systems can predict the optimal surveillance measures for diseasemanagement. The HS system can identify outbreaks as early as possible totrack and contain spread to enable appropriate, rapid mitigationstrategies to be put into place. The model for a given setting can beoptimized to take into account various factors to provide optimalsurveillance and mitigation strategy. One factor includes prioritizingtesting based on risk factors and symptoms, including prioritizingtesting of infants, children, pregnant women, medical personnel, highrisk individual and geriatrics. Another factor includes testing closecontacts of index cases, such as targeting testing at household,schools, and workplaces where there are confirmed or suspected cases. Inaddition, the system as assess the impact of alternate diagnostic testsbased on various factors, such as sensitivity, specificity, turn around(i.e., time to get results from an assay). In some embodiments, theassays performed comprise one or more of cartridge assays, real timePCR, rapid antigen tests, viral culture, and immunoassays. In someembodiments, a less expensive assay may be used for a large number ofsecondary assays to minimize expense. Based on these data, a smallernumber of more expensive, but more sensitive and specific assays can beused to test selected individuals.

When suspected infected individuals are detected by the HS, whether theindividual is symptomatic or asymptomatic, assays can be performed inthe field with the FS and the results and location of the subject can berelayed to the OS, e.g., at a central server at a central monitoringsite. At the monitoring site, the results can be displayed and alertsregistered if appropriate so that containment efforts, including furtherdeployment and testing of FS components, can be initiated. In someembodiments, the model contained in the software will automaticallysuggest where the disease is likely to spread and where resources willneed to be deployed to contain the disease and do further in-fieldmonitoring. The system can contact individuals involved in surveillance,e.g., government or healthcare workers, e.g., by phone, pager, fax,email, text message, or other rapid form of communication. In someembodiments, the data and analysis provided by the HS is provided toofficials and health care professionals, not to individual users. Thishelps ensure that medical decision making is made appropriately.

An advantage of the Health Shield as described herein is that assayresults from the field systems can be substantially immediatelycommunicated to any third party that may benefit from obtaining theresults. For example, once results of a measurement taken by an FSdevice are communicated to the OS, an analyte concentration can bedetermined at the Operating System component and transmitted to anindividual or to medical personnel who may need to take further action.This might include identification of an index case. The communicationstep to a third party can be performed wirelessly as described herein,and by transmitting the data to a third party's hand held device, thethird party can be notified of the assay results virtually anytime andanywhere. Thus, in a time-sensitive scenario, a patient may be contactedimmediately anywhere if urgent medical action may be required.

The systems of the invention can be designed to interface with anycombination of different Electronic Health Record (EHR) systems and anyother relevant databases. Moreover, the system can be configured toautomatically translate data that currently exists in different formatsinto one standard format. Once the system imports and translates thedata, it can centralize the information into one or more repositoriesand pass the imported data through predictive models. In this manner,the system can compile and take advantage of multiple data sources tobest model the outbreak and predict appropriate containment responses.Those models learn from every new data point, becoming increasinglypredictive over time. In some embodiments, the models recognize patternsthat predict how a given individual's disease is likely to progress.

A pilot program can be used to help refine the system parameters. Insome embodiment, an initial screening and containment strategy isdeveloped. The HS is then deployed to pilot that model in a region ofinterest, e.g., a township, neighborhood, hospital or commercial area.With this pilot the robustness of the assumptions underlying themodeling effort can be tested, and the containment strategy can be finetuned. In some embodiments, the fine tuning is performed automaticallyby the learning algorithms of the OS. For example, the modeling softwarecontains pattern recognition technologies that allow the algorithmsforecasting the spread of the disease to be continually refined withevery new data-point sent to the software portal. As such, the systembecomes increasingly predictive over time. In some embodiments, theserefinements continue even after the system is deployed after the pilotstages.

After a system is developed using historical data, archived samples andeven the pilot phase, the systems can be placed in strategic locationsto begin preventing the spread of any outbreak. Because each instrumentcan process different cartridges that can be customized for a givendisease of interest, e.g., with a specific strain of influenza thatpresents concern, the same systems can be used to contain and preventthe spread of a virus even if it mutates. In some embodiments, thecartridges contain protein-based tests which measure inflammation andresponse to infection allowing officials to recognize severe infectioneven if the virus mutates, and specific tests for new viral trains canimmediately be developed and deployed through the existinginfrastructure and instruments. In addition, the same instrumentsdeployed to monitor infectious disease are available to then monitorother health-related issues such as diabetes, obesity, cardiovasculardisease and oncology concerns, e.g., cancer therapy. Differentcartridges and additional models for the software can be customizedaround the HS systems already in place. Validation data for eachapplication can be performed prior to deployment and adjustedprospectively by learning from the incoming data.

Noncompliance with the recommended treatment can undermine the efficacyof the containment strategy of the present invention. As such, in someembodiments the system of the present invention can be used to monitorpatient compliance and notify the patient or other medical personnel ofsuch noncompliance. For example, a patient taking a pharmaceutical agentas part of medical treatment plan can take a bodily fluid sample whichis assayed as described herein, but a metabolite concentration, forexample, detected by the system may be at an elevated level compared toa known profile that will indicate multiple doses of the pharmaceuticalagent have been taken. The patient or medical personnel may be notifiedof such noncompliance via any method discussed herein, including withoutlimitation notification via a handheld device such a PDA or cell phone,or through a third party such as a healthcare worker who also receivescommunication of the noncompliance. Such a known profile may be locatedor stored on an external device described herein.

In an embodiment, the system can be used to identify sub-populations ofpatients which are benefited or harmed by a therapy. In this way, drugswith potential toxicity can be administered to only to those who willbenefit.

In terms of pharmaceutical-related adverse events, the Health Shieldsystems can be placed in an individual's residence. In some embodiments,the HS is used to monitor safety and efficacy of treatments for acuteconditions, e.g., debilitating or life threatening illnesses, or forchronic conditions. The FS components can also be placed in centrallocations such as pharmacies such that individuals can be tested whenfilling prescriptions.

Case studies have been performed for diabetes, infection, and oncologyconsidering the needs of governmental disease management systems as wellas healthcare corporations. One such study was aimed at a model forpreventing and reversing diabetes. The modeled data demonstrateddramatic cost savings associated with eliminating the centralizedinfrastructure for blood and data analysis of health information andinstead using the systems of the present invention with FS systemsplaced at various points of care, including the home environment. Thesystem provided savings in part by limitation of shipping costs,reduction of personnel costs associated with running analysis, reductionof costs associated with false positives, reduction of time associatedwith waiting for results. In various modeling environments, the HSsystem would reduce the costs associated with conventional testing bygreater than an estimated 50%, in addition to the value of time-saved inacquiring the relevant data.

5. Monitoring Influenza Outbreaks

In one aspect, the systems of the invention are deployed to monitor andcontain disease outbreaks. The HS is particularly beneficial in theinfluenza setting because containment strategies that initially rely onmass vaccination programs may not be sufficiently effective to containan outbreak. Influenza A virus strains are categorized according to twoproteins found on the surface of the virus: hemagglutinin (H) andneuraminidase (N). All influenza A viruses contain these two surfaceproteins, but the structures of these proteins differ between virusstrains, due to rapid genetic mutation in the viral genome. There are 16H and 9 N subtypes known in birds, but only a subset, e.g., H 1, 2 and3, and N 1 and 2, are commonly found in humans. The pathogenicity of astrain varies among subtype. For example, the H5N1 strain, commonlyreferred as “avian flu” or “bird flu,” most commonly affects birds but arecent outbreak of the strain in humans in Asia killed up to 60% ofthose infected.

Although flu vaccines can help prevent spread, the changing subtypes andmutations of the flu makes vaccination only a partial solution. Forexample, the H1N1 influenza virus, commonly referred to as the SwineFlu, is responsible for the 2009 pandemic. Like H5N1, H1N1 can bevirulent in humans. The United States Center for Disease Control andPrevention (CDC) maintains information about the 2009 H1N1 pandemic atwww.cdc.gov/H1N1FLU/. The CDC is concerned that the new H1N1 flu viruscould result in a particularly severe flu season in 2009, e.g., throughwidespread illness, doctor's visits, hospitalizations and deaths. Thefirst H1N1 vaccine will not be available before mid-October at theearliest, and vaccine supplies will not be sufficient to treat even themost at-risk populations until later in the fall. As a result, the bestway to prevent a widespread epidemic and public panic will be to controlthe virus by preventing its spread, particularly to those who are athighest risk.

Some governments have been trying flu containment methods that wereeffective with Severe Acute Respiratory Syndrome (SARS), includingscreening for fever or respiratory symptoms. However, those methods arenot sufficiently targeted to contain H1N1. One problem is the fluvictims can be contagious at least a day before a fever or othersymptoms present. In some embodiments, the Health Shield of theinvention systematically tests not only those who are symptomatic butalso family members and close work associates. Accordingly, infectedindividuals can be treated and isolated before they have an opportunityto spread the infection widely, reducing the flu's real andpsychological impact.

The spread and the death rate from flu in the fall of 2009 would bemitigating by keeping patients from flooding emergency rooms for testingand treatment. Potentially hundreds of millions of dollars can be savedby reducing costly emergency room and hospital visits, by proper use ofmedication, and by reducing virus spread in hospitals. The HS models ofthe invention can identify optimal intervention strategies and timingfor administration of appropriate medication, such as Tamiflu. Thesesteps can reduce hospital and emergency room visits and allow people toresume work more quickly. Eliminating such unnecessary emergency roomvisits can help prevent the spread of the virus and reducehospitalization and emergency room spending.

Influenza, e.g., H1N1 and H5N1, can be detected from a bodily fluid,e.g., a finger-stick of blood, sputum, saliva, or a combination thereof,using FS point-of-care instruments. These instruments can be placed inappropriate locations (e.g., home, schools, restaurants, primary careunits, live-stock facilities etc.) and can be deployed in many caseswithout local supporting infrastructure other than a power source. Thetesting can be done rapidly, e.g., in less than about 1, 2, 3, 4, 5, 10,15, 20, 25, 30, 35, 40, 45, 50, 55 or 60 minutes. In some embodiments,the results from the FS are reported back to an OS central monitoringsite in real-time. The blood or saliva based assays can detect influenzaby several methods, including immunodetection by sensitive antibodies ofspecific epitopes of the virus itself, e.g., hemagglutinin and/orneuraminidase. The assays can distinguish between the various types ofidentified influenza strains, e.g., influenza A, influenza B, H5N1,H1N1, etc. The assays can detect individual particles of a particularvirus strain, even in a background of differing strains or geneticvariants. The assays can detect biomarkers, viral proteins, coatproteins, and the like.

In some embodiments, the assays measure inflammatory markers and immuneresponse markers, e.g., cytokines, which allow for clinicians toidentify the severity of infection, the extent of the acute phase and/orinflammatory reactions of the subject. This can, e.g., assist indetermining the proper treatment regimen for an individual. The abilityto measure response to infection allows for characterization ofinfection even to strains of viruses that have not yet beencharacterized. As those strains are characterized, specific tests can becustomized and added to the cartridges. Depending on the assay required,the new tests can be deployed immediately, within days, within weeks, orwithin a matter of months.

There are currently over 100 cytokines/chemokines whose coordinate ordiscordant regulation is of clinical interest. Exemplary cytokines thatcan be used in systems and methods of the invention include, but are notlimited to, BDNF, CREB pS133, CREB Total, DR-5, EGF, ENA-78, Eotaxin,Fatty Acid Binding Protein, FGF-basic, granulocyte colony-stimulatingfactor (G-CSF), GCP-2, Granulocyte-macrophage Colony-stimulating FactorGM-CSF (GM-CSF), growth-related oncogene-keratinocytes (GRO-KC), HGF,ICAM-1, IFN-alpha, IFN-gamma, the interleukins IL-10, IL-11, IL-12,IL-12 p40, IL-12 p40/p70, IL-12 p70, IL-13, IL-15, IL-16, IL-17, IL-18,IL-1alpha, IL-1beta, IL-bra, IL-1ra/IL-1F3, IL-2, IL-3, IL-4, IL-5,IL-6, IL-7, IL-8, IL-9, interferon-inducible protein (10 IP-10),JE/MCP-1, keratinocytes (KC), KC/GROa, LIF, Lymphotacin, M-CSF, monocytechemoattractant protein-1 (MCP-1), MCP-1 (MCAF), MCP-3, MCP-5, MDC, MIG,macrophage inflammatory (MIP-1 alpha), MIP-1 beta, MIP-1 gamma, MIP-2,MIP-3 beta, OSM, PDGF-BB, regulated upon activation, normal T cellexpressed and secreted (RANTES), Rb (pT821), Rb (total), Rb pSpT249/252,Tau (pS214), Tau (pS396), Tau (total), Tissue Factor, tumor necrosisfactor-alpha (TNF-alpha), TNF-beta, TNF-RI, TNF-RII, VCAM-1, and VEGF.In some embodiments, the cytokine is IL-12p70, IL-10, IL-1 alpha, IL-3,IL-12 p40, IL-1ra, IL-12, IL-6, IL-4, IL-18, IL-10, IL-5, eotaxin,IL-16, MIG, IL-8, IL-17, IL-7, IL-15, IL-13, IL-2R (soluble), IL-2,LIF/HILDA, IL-1 beta, Fas/CD95/Apo-1, and MCP-1.

Markers of inflammation that can be used with the systems and methods ofthe invention include ICAM-1, RANTES, MIP-2, MIP-1-beta, MIP-1-alpha,and MMP-3. Further markers of inflammation include adhesion moleculessuch as the integrins α1β1, α2β1, α3β1, α4β1, α5β1, α6β1, α7β1, α8β1,α9β1, αVβ7, α4β7, α6β4, αDβ2, αLβ2, αMβ2, αVβ3, αVβ5, αVβ6, αVβ8, αXβ2,αIIβ3, αIELbβ7, beta-2 integrin, beta-3 integrin, beta-2 integrin,beta-4 integrin, beta-5 integrin, beta-6 integrin, beta-7 integrin,beta-8 integrin, alpha-1 integrin, alpha-2 integrin, alpha-3 integrin,alpha-4 integrin, alpha-5 integrin, alpha-6 integrin, alpha-7 integrin,alpha-8 integrin, alpha-9 integrin, alpha-D integrin, alpha-L integrin,alpha-M integrin, alpha-V integrin, alpha-X integrin, alpha-IIbintegrin, alphaIELb integrin; Integrin-associated Molecules such as BetaIG-H3, Melusin, CD47, MEPE, CD151, Osteopontin, IBSP/Sialoprotein II,RAGE, IGSF8; Selectins such as E-Selectin, P-Selectin, L-Selectin; andLigands such as CD34, GlyCAM-1, MadCAM-1, PSGL-1, vitronectic,vitronectin receptor, fibronectin, vitronectin, collagen, laminin,ICAM-1, ICAM-3, BL-CAM, LFA-2, VCAM-1, NCAM, and PECAM. Further markersof inflammation include cytokines such as IFN-α, IFN-β, IFN-ε, -κ, and-τ, and -ξ IFN-ω, IFN-γ, IL29, IL28A and IL28B, IL-1, IL-1α and β, IL-2,IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-11, IL-12, IL-13,IL-14, IL-15, IL-16, IL-17, IL-18, IL-19, IL-20, IL-21, IL-22, IL-23,IL-24, IL-25, IL-26, IL-27, IL-28, IL-29, IL-30, and TCCR/WSX-1. Furthermarkers of inflammation include cytokine receptors such as Common betachain, IL-3 R alpha, IL-3 R beta, GM-CSF R, IL-5 R alpha, Common gammaChain/IL-2 R gamma, IL-2 R alpha, IL-9 R, IL-2 R beta, IL-4 R, IL-21 R,IL-15 R alpha, IL-7 R alpha/CD127, IL-1ra/IL-1F3, IL-1 R8, IL-1 R1, IL-1R9, IL-1 R11, IL-18 R alpha/IL-1 R5, IL-1 R3/IL-1 R AcP, IL-18 Rbeta/IL-1 R7, IL-1 R4/ST2 SIGIRR, IL-1 R6/IL-1 R rp2, IL-11 R alpha,IL-31 RA, CNTF R alpha, Leptin R, G-CSF R, LIF R alpha, IL-6 R, OSM Rbeta, IFN-alpha/beta R1, IFN-alpha/beta R2, IFN-gamma R1, IFN-gamma R2,IL-10 R alpha, IL-10 R beta, IL-20 R alpha, IL-20 R beta, IL-22 R, IL-17R, IL-17 RD, IL-17 RC, IL-17B R, IL-13 R alpha 2, IL-23 R, IL-12 R beta1, IL-12 R beta 2, TCCR/WSX-1, and IL-13 R alpha 1. Further markers ofinflammation include chemokines such as CCL-1, CCL-2, CCL-3, CCL-4,CCL-5, CCL-6, CCL-7, CCL-8, CCL-9, CCL-10, CCL-11, CCL-12, CCL-13,CCL-14, CCL-15, CCL-16, CCL-17, CCL-18, CCL-19, CCL-20, CCL-21, CCL-22,CCL-23, CCL-24, CCL-25, CCL-26, CCL-27, CCL-28, MCK-2, MIP-2, CINC-1,CINC-2, KC, CINC-3, LIX, GRO, Thymus Chemokine-1, CXCL-1, CXCL-2,CXCL-3, CXCL-4, CXCL-5, CXCL-6, CXCL-7, CXCL-8, CXCL-9, CXCL-10,CXCL-11, CXCL-12, CXCL-13, CXCL-14, CXCL-15, CXCL-16, CXCL-17, XCL1,XCL2, and Chemerin. Further markers of inflammation include chemokinereceptors such as CCR-1, CCR-2, CCR-3, CCR-4, CCR-5, CCR-6, CCR-7,CCR-8, CCR-9, CCR-10, CXCR3, CXCR6, CXCR4, CXCR1, CXCR5, CXCR2, ChemR23. Further markers of inflammation include Tumor necrosis factors(TNFs), such as TNFα, 4-1BB Ligand/TNFSF9, LIGHT/TNFSF14, APRIL/TNFSF13,Lymphotoxin, BAFF/TNFSF13B, Lymphotoxin beta/TNFSF3, CD27 Ligand/TNFSF7,OX40 Ligand/TNFSF4, CD30 Ligand/TNFSF8, TL1A/TNFSF15, CD40Ligand/TNFSF5, TNF-alpha/TNFSF1A, EDA, TNF-beta/TNFSF1B, EDA-A2,TRAIL/TNFSF10, Fas Ligand/TNFSF6, TRANCE/TNFSF11, GITR Ligand/TNFSF18,and TWEAK/TNFSF12. Further markers of inflammation include TNFSuperfamily Receptors such as 4-1BB/TNFRSF9, NGF R/TNFRSF16, BAFFR/TNFRSF13C, Osteoprotegerin/TNFRSF11B, BCMA/TNFRSF17, OX40/TNFRSF4,CD27/TNFRSF7, RANK/TNFRSF11A, CD30/TNFRSF8, RELT/TNFRSF19L,CD40/TNFRSF5, TACI/TNFRSF13B, DcR3/TNFRSF6B, TNF R1/TNFRSF1A, DcTRAILR1/TNFRSF23, TNF R11/TNFRSF1B, DcTRAIL R2/TNFRSF22, TRAIL R1/TNFRSF10A,DR3/TNFRSF25, TRAIL R2/TNFRSF10B, DR6/TNFRSF21, TRAIL R3/TNFRSF10C,EDAR, TRAIL R4/TNFRSF10D, Fas/TNFRSF6, TROY/TNFRSF19, GITR/TNFRSF 18,TWEAK R/TNFRSF 12, HVEM/TNFRSF 14, and XEDAR. Further markers ofinflammation include TNF Superfamily Regulators such as FADD, TRAF-2,RIP1, TRAF-3, TRADD, TRAF-4, TRAF-1, and TRAF-6. Further markers ofinflammation include acute-phase reactants and acute phase proteins.Further markers of inflammation include TGF-beta superfamily ligandssuch as Activins, Activin A, Activin B, Activin AB, Activin C, BMPs(Bone Morphogenetic Proteins), BMP-2, BMP-7, BMP-3, BMP-8,BMP-3b/GDF-10, BMP-9, BMP-4, BMP-10, BMP-5, BMP-15/GDF-9B, BMP-6,Decapentaplegic, Growth/Differentiation Factors (GDFs), GDF-1, GDF-8,GDF-3, GDF-9 GDF-5, GDF-11, GDF-6, GDF-15, GDF-7, GDNF Family Ligands,Artemin, Neurturin, GDNF, Persephin, TGF-beta, TGF-beta, TGF-beta 3,TGF-beta 1, TGF-beta 5, LAP (TGF-beta 1), Latent TGF-beta bp1, LatentTGF-beta 1, Latent TGF-beta bp2, TGF-beta 1.2, Latent TGF-beta bp4,TGF-beta 2, Lefty, MIS/AMH, Lefty-1, Nodal, Lefty-A, Activin RIA/ALK-2,GFR alpha-1/GDNF R alpha-1, Activin RIB/ALK-4, GFR alpha-2/GDNF Ralpha-2, Activin RIIA, GFR alpha-3/GDNF R alpha-3, Activin RIIB, GFRalpha-4/GDNF R alpha-4, ALK-1, MIS R11, ALK-7, Ret, BMPR-IA/ALK-3,TGF-beta R1/ALK-5, BMPR-IB/ALK-6, TGF-beta R11, BMPR-II, TGF-beta RIIb,Endoglin/CD105, and TGF-beta RIII. Further markers of inflammationinclude TGF-beta superfamily Modulators such as Amnionless, NCAM-1/CD56,BAMBI/NMA, Noggin, BMP-1/PCP, NOMO, Caronte, PRDC, Cerberus 1, SKI,Chordin, Smad1, Chordin-Like 1, Smad2, Chordin-Like 2, Smad3, COCO,Smad4, CRIM1, Smad5, Cripto, Smad7, Crossveinless-2, Smad8, Cryptic,SOST, DAN, Latent TGF-beta bp1, Decorin, Latent TGF-beta bp2, FLRG,Latent TGF-beta bp4, Follistatin, TMEFF1/Tomoregulin-1, Follistatin-like1, TMEFF2, GASP-1/WFIKKNRP, TSG, GASP-2/WFIKKN, TSK, Gremlin, andVasorin. Further markers of inflammation include EGF Ligands such asAmphiregulin, LRIG3, Betacellulin, Neuregulin-1/NRG1, EGF,Neuregulin-3/NRG3, Epigen, TGF-alpha, Epiregulin, TMEFF1/Tomoregulin-1,HB-EGF, TMEFF2, and LRIG1. Further markers of inflammation include EGFR/ErbB Receptor Family, such as EGF R, ErbB3, ErbB2, and ErbB4. Furthermarkers of inflammation include Fibrinogen. Further markers ofinflammation include SAA. Further markers of inflammation include glialmarkers, such as alpha.1-antitrypsin, C-reactive protein (CRP),α2-macroglobulin, glial fibrillary acidic protein (GFAP), Mac-1, andF4/80. Further markers of inflammation include myeloperoxidase. Furthermarkers of inflammation include Complement markers such as C3d, C1q, C5,C4d, C4 bp, and C5a-C9. Further markers of inflammation include Majorhistocompatibility complex (MHC) glycoproteins, such as HLA-DR andHLA-A,D,C. Further markers of inflammation include Microglial markers,such as CR3 receptor, MHC I, MHC II, CD 31, CD11a, CD11b, CD11c, CD68,CD45RO, CD45RD, CD18, CD59, CR4, CD45, CD64, and CD44. Further markersof inflammation include alpha.2 macroglobulin receptor, Fibroblastgrowth factor, Fc gamma R1, Fc gamma R11, CD8, LCA (CD45), CD18, CD59,Apo J, clusterin, type 2 plasminogen activator inhibitor, CD44,Macrophage colony stimulating factor receptor, MRP14, 27E10,4-hydroxynonenal-protein conjugates, IκB, NFκB, cPLA₂, COX-2, Matrixmetalloproteinases, Membrane lipid peroxidation, and ATPase activity.HSPC228, EMP1, CDC42, TLE3, SPRY2, p40BBP, HSPC060 and NAB2, or adown-regulation of HSPA1A, HSPA1B, MAPRE2 and OAS1 expression,TACE/ADAM17, alpha-1-Acid Glycoprotein, Angiopoietin-1, MIF,Angiopoietin-2, CD14, beta-Defensin 2, MMP-2, ECF-L/CHI3L3, MMP-7, EGF,MMP-9, EMAP-II, MSP, EN-RAGE, Nitric Oxide, Endothelin-1,Osteoactivin/GPNMB, FPR1, PDGF, FPRL1, Pentraxin 3/TSG-14, FPRL2, Gas6,PLUNC, GM-CSF, RAGE, S100A10, S100A8, S100A9, HIF-1 alpha, Substance P,TFPI, TGF-beta 1, TIMP-1, TIMP-2, TIMP-3, TIMP-4, TLR4, LBP, TREM-1,Leukotriene A4, Hydrolase TSG-6, Lipocalin-1, uPA, M-CSF, and VEGF.

Physiological data for each individual can also be measured andcommunicated from the FS instruments or points-of-care to the OS. Suchdata can include without limitation temperature, heart rate/pulse, bloodpressure, oximetric signals, weight, water retention, plethysmographicsignals, respiratory rate, fat content, water content, blood perfusion,mobility, posture, bioelectric impedance, electrocardiogram (ECG), orgalvanic skin response.

In some embodiments, the assays are used to detect host antibodiesagainst a particular pathogen or marker. One potential problem whenmeasuring such antibodies is interference which can occur in individualswho had flu vaccinations in the past. In such situations, high influenzaantibody titers in the blood may interfere with the assay. Flu virusmainly replicates in lungs and therefore may be detected in, e.g.,sputum, nasal lavage and saliva. Therefore, a saliva based sample canalso be processed in the point-of-care for verification. Thehemagglutinin (H antigen) antigen on the surface of influenza particlesis believed to be instrumental in the entry of the virus into targetcells. Hemagglutinin can bind red cells and in appropriate conditionscauses the cells to agglutinate. Accordingly, red cells in blood can actas concentrating agents for the virus. This phenomenon can be exploitedin assays for the virus since red cells can be concentrated before ablood sample is analyzed. Furthermore red cells can be collected (andconcentrated) on an appropriate surface in an assay cartridge, therebypresenting large amounts of virus for analysis and detection.

Two key evaluative measures of any medical screening or diagnostic testare its sensitivity and specificity, which measure how well the testperforms to accurately detect all affected individuals withoutexception, and without falsely including individuals who do not have thetarget disease (predictive value).

A true positive (TP) result is where the test is positive and thecondition is present. A false positive (FP) result is where the test ispositive but the condition is not present. A true negative (TN) resultis where the test is negative and the condition is not present. A falsenegative (FN) result is where the test is negative but the condition isnot present. In this context: Sensitivity=TP/(TP+FN);Specificity=TN/(FP+TN); and Predictive value of a positive=TP/(TP+FP).

Sensitivity is a measure of a test's ability to correctly detect thetarget disease in an individual being tested. A test having poorsensitivity produces a high rate of false negatives, i.e., individualswho have the disease but are falsely identified as being free of thatparticular disease. The potential danger of a false negative is that thediseased individual will remain undiagnosed and untreated for someperiod of time, during which the disease may progress to a later stagewherein treatments, if any, may be less effective. An example of a testthat has low sensitivity is a protein-based blood test for HIV. Thistype of test exhibits poor sensitivity because it fails to detect thepresence of the virus until the disease is well established and thevirus has invaded the bloodstream in substantial numbers. In contrast,an example of a test that has high sensitivity is viral-load detectionusing the polymerase chain reaction (PCR). High sensitivity is achievedbecause this type of test can detect very small quantities of the virus.High sensitivity is particularly important when the consequences ofmissing a diagnosis are high.

Specificity, on the other hand, is a measure of a test's ability toidentify accurately patients who are free of the disease state. A testhaving poor specificity produces a high rate of false positives, i.e.,individuals who are falsely identified as having the disease. A drawbackof false positives is that they force patients to undergo unnecessarymedical treatments with their attendant risks, emotional and financialstresses, and which could have adverse effects on the patient's health.Specificity is important when the cost or risk associated with furtherdiagnostic procedures or further medical intervention is very high.

In some embodiments, the HS performs multiple assays to improve assaysensitivity and/or specificity. For example, the sensitivity andspecificity of disease monitoring can be enhanced. In some embodiments,multiple bodily samples are assayed for an individual. For example,saliva and blood based (finger-stick) tests can be run simultaneouslyfor persons who have previously been vaccinated for the flu. Testingmultiple samples can increase the chance of identifying the infection.In addition, it can be important to control for false negatives tomaximize containment. In some embodiments, the present invention addressfalse negatives by including tests for both inflammation and infectionmarkers on each test cartridge. Where the flu test is negative but theseother markers are strongly suggestive of flu, confirmatory tests can beincluded for that specific subset of patients. A variety of exemplarymarker panels, also referred to as test menus, are disclosed herein forvarious disease settings. One of skill will appreciate that the use ofmultiple assays and/or physiological parameters to improve sensitivityand/or specificity is not limited to these exemplary embodiments butrather can be an effective technique when monitoring many diseases anddisorders.

In some embodiments, the HS decentralized detection capability providedby the FS units can provide early identification of persons with aconfirmed case of flu, i.e., an “index case,” and then query all closecontacts that those individuals so identified. Given such a network ofcontacts, containing epidemic spread ideally requires rapid deployment,identification, and preemptive action in an exposed and/orasymptomatically infected population. The HS provides a system to carryout these operations and prevent the spread of disease.

The Health Shield system can be deployed for the surveillance andcontainment of an influenza outbreak. The HS can be deployed in avariety of settings, e.g., at a local, regional or national level. TheOS for a given setting can use in silico modeling to simulate variousdeployment strategies to best contain the flu or other condition and canbe optimized for each setting. In some embodiments, the model comprisesan epidemiological model that includes a variety of appropriateparameters to model the expected and/or contained outbreak. In someembodiments, the system uses Monte Carlo simulations to test a spectrumof screening and containment strategies which will, in turn, be analyzedas to cost/benefit ratios, etc. For example, the system can projectwhere and how to deploy limited resources, e.g., medical personnel,therapeutic treatments and vaccines. The OS model can be preloaded withpopulation and individual specific information for the setting to bemonitored. These factors include but are not limited to incubation time,connectivity of the susceptible population, manner of infection,virulence of the virus, death rates and hospitalization rates, diseaseincidence, transmission mode, infection rate, therapeutic interventionoutcomes, vaccine efficacy, and resistance to or effectiveness ofanti-viral therapies, e.g., Tamiflu. Parameters for the individualsbeing monitored include without limitation age, sex, social contacts(living arrangements, family, co-workers, etc.), prior history ofdisease, general health (e.g., other pre-existing conditions), etc.Model parameters can be continuously updated once the system isdeployed.

The FS instruments are deployed to operate in conjunction with theconfigured OS. In some embodiments, the data from the FS are provided toan OS through a software portal. The remote OS can then perform thedesired calculations. In general, the FS systems are deployed toselected hotspots. In some embodiments, the OS model is used to directthe optimal deployment of the FS instruments. Optimal and hotspotlocations include without limitation areas where people gather, e.g.,shopping areas, schools and work places. Locations where sick peoplegather are also targeted, including without limitation clinics,pharmacies and hospitals. In some embodiments, FS devices are deployedto homes, as described herein.

Once deployed, the FS systems are used to test the subjects. In someembodiments, this includes testing for disease antigens, e.g., viralcoat proteins. The analytes also include host proteins as markers ofdisease, e.g., immune markers including cytokines, and inflammatorymarkers that indicate an ongoing infection. In detecting infectiousdisease agents and evaluating the status and prognosis of patients, itcan be desirable to be able to measure multiple analytes simultaneously.For example, this increases the chance of detecting disease as any onesingle analyte may not be found at abnormal levels. Multiple analytemeasurements also reduce noise and can make the system more accurate indisease monitoring.

The following table presents an example menu for detection of H1N1virus, also known as swine flu:

TABLE 3 Marker Sample Indication H1 Blood/Sputum/Saliva/Nasal lavageInfection N1 Blood/Sputum/Saliva/Nasal lavage Infection H1:N1Blood/Sputum/Saliva/Nasal lavage Infection IgM anti-H1 Blood Primaryresponse to infection IgM anti-N1 Blood Primary response to infectionIgG anti-H1 Blood Prior infection IgG anti-N1 Blood Prior infection IgAanti-H1 Sputum/Saliva/Nasal lavage Prior infection IgA anti-N1Sputum/Saliva/Nasal lavage Prior infection IgG anti-H1:H1 Blood Prior +current infection IgG anti-N1:N1 Blood Prior + current infectionCytokines Blood Acute process C-Reactive Blood Acute process Protein

In the table, “Ab:Ag” represents the complex formed between an antibody(Ab) and an antigen (Ag). For example, “IgG anti-H1:H1” represents acomplex between host IgG anti-H1 antibodies and influenza hemagluttininH1 antigens. As different influenza strains are monitored, the menu willbe adjusted accordingly. For example, a menu for monitoring H1N5 viruswould comprise detection of N5 antigen and anti N5 antibodies.

Detection of IgM versus IgG or IgA antibodies can be used to determinewhether an individual had a prior exposure to the influenza particles ofinterest. IgM antibodies are made rapidly in the days followinginfection on the first exposure to an immunogen. When previously exposedindividuals encounter a second infectious agent having similar oridentical antigenic character, IgG and IgA antibodies are produced veryrapidly. This secondary response is typically much stronger and morespecific than the original IgM response. In primary infections and invery severe infections, active virus is more likely to be present inblood and to be detectable directly. In secondary infections, whereantibody is present, it will generally be in excess over the antigen andantigen may be masked to immunoassay methods. In some embodiments, thecomplex formed by antigen and antibody is detected using a sandwichimmunoassay in which one reagent is directed to the antigen and theother to IgG. Once a subject produces IgG and IgA antibodies, such maybe found in the blood well after the infection has resolved.

As shown in Table 3, the menu can also include one or more cytokines asa marker of immune response and/or inflammation. Cytokines of interestinclude without limitation IL-1β, IL-6, IL-8, IL-10 and TNFα. Cytokinessuch as these may be produced in large amounts during the early part ofa viral infection. In some cases, the level of these markers will riseand fall rapidly. Valuable information as to patient status andprognosis can be obtained by making serial measurements of one or morecytokines. For example, fevers of viral and bacterial origin may bedistinguished by measuring changes in cytokine levels. A recent studyfound that “CRP velocity” (CRPv), defined as the ratio between bloodC-reactive protein on admission to an Emergency Room and the number ofhours since the onset of fever, can differentiate acute bacterial andnon-bacterial febrile illnesses. Paran et al., C-reactive proteinvelocity to distinguish febrile bacterial infections from non-bacterialfebrile illnesses in the emergency department, Crit. Care. 2009;13(2):R50. The study also found that blood levels of other acute-phaseproteins, such as IL-1, IL-6, and TNF-α, correlated with CRPv.

The detection levels of influenza markers is shown in Table 4:

TABLE 4 Threshold or action levels for influenza bio-markers MarkerSample Level H1 Blood/Sputum/Saliva/Nasal lavage ng/mL N1Blood/Sputum/Saliva/Nasal lavage ng/mL H1:N1 Blood/Sputum/Saliva/Nasallavage ng/mL IgM anti-H1 Blood ug/mL IgM anti-N1 Blood ug/mL IgG anti-H1Blood ug/mL IgG anti-N1 Blood ug/mL IgA anti-H1 Sputum/Saliva/Nasallavage ug/mL IgA anti-N1 Sputum/Saliva/Nasal lavage ug/mL IgG anti-H1:H1Blood ug/mL IgG anti-N1:N1 Blood ug/mL Cytokines Blood 10 x increaseC-Reactive Protein Blood 10 x increase

The exemplary markers in Tables 3 and 4 correspond to a menu fordetection of H1N1. The threshold levels for detecting a certain markerare shown in Table 4. When the measurements are made over a time course,the fold increase in a marker, e.g., cytokines or C-reactive protein,can be detected. Here, a 10× change is considered indicative of anevent. When time course data for an individual is not available, thefold-change can be determined by comparing to a reference threshold. Forexample, the detected level of a given marker can be compared to themean level of the marker in the general healthy population. It will beappreciated that different flu strains, e.g., H5N1, H3N2, etc, can bedetected using appropriate analytical methods.

An OS recommended course action for influenza when detecting a givenmarker is shown in Table 5.

TABLE 5 Suspected swine flu action matrix Marker Sample IndicationAction H1 Blood/Sputum/ Infection Quarantine Saliva/Nasal lavage N1Blood/Sputum/ Infection Quarantine Saliva/Nasal lavage H1:N1Blood/Sputum/ Infection Quarantine Saliva/Nasal lavage IgM anti-H1 Blood1o response to Quarantine infection IgM anti-N1 Blood 1o response toQuarantine infection IgG anti-H1 Blood Prior infection None IgG anti-N1Blood Prior infection None IgA anti-H1 Sputum/Saliva/ Prior infectionNone Nasal lavage IgA anti-N1 Sputum/Saliva/ Prior infection None Nasallavage IgG anti-H1:H1 Blood Prior + current Quarantine infection IgGanti-N1:N1 Blood Prior + current Quarantine infection Cytokines BloodAcute process Quarantine C-Reactive Protein Blood Acute processQuarantine Drug resistance Any Viral mutation Special Virulence gene(s)Any Dangerous Special viral strain Temperature rise NA InfectionQuarantine

As above, the example in Table 5 highlights H1N1 swine flu. It will beappreciated that different flu strains, e.g., H5N1, H3N2, etc, can bedetected using appropriate analytical methods. Further, the action willdepend on a number of factors, including but not limited to expectedvirulence, transmission, cost of treatment, etc. For example, aquarantine may be required for a virulent strain but not for a lesssevere outbreak. The recommended course of action for drug resistancecan depend on the drug. In the influenza setting, resistance tooseltamivir (Tamiflu®) can be especially important. Oseltamivir is anorally active antiviral drug that acts as a neuraminidase inhibitor. Thedrug slows the spread of influenza (flu) virus between cells in the bodyby stopping the new virus from chemically cutting ties with its hostcell. It can be used for both influenza A and B. Resistance can bedetermined by a number of methods, e.g., a functional assay (culture) oridentification of a genetic marker. Zanamivir is also used to treat fluinfection.

Specific strains of influenza virus can be detected using a sandwichassay format. A number of assay configurations can be used. FIG. 3illustrates assays for H1N1 antigen illustrating sandwich complexes infour different assay types. One of skill will understand that a similararrangement can be used to detect other virus strains, e.g., H5N1, H2N3,etc. Shown are the final reaction products for four assay configurationsfor measurement of H1N1 virus (having several copies of each on theviral particle). The assays involve: 1) adding sample, e.g., blood,serum, saliva or nasal lavage, to a capture surface having an antibodyto one of the viral surface antigens (H1, N1); 2) adding enzyme-labeledantibody to one of the surface antigens; and 3) washing the surface toremove unbound viral particles. The different assay configurations candetect various particles. Configurations α-H1/α-N1 and α-N1/α-H1 willmeasure H1N1 virus, configuration α-H1/α-H1 detects any virus having H1antigen, and configuration αi-N1/α-N1 detects any virus having the N1antigen. A cartridge system to detect the assays is described in U.S.patent application Ser. No. 11/746,535, filed May 9, 2007 and entitled“REAL-TIME DETECTION OF INFLUENZA VIRUS.”

Sandwich assays can also be used to detect host antibodies to influenzastrains, e.g., human antibodies to H1N1 swine flu. A first embodiment ofsuch assay is shown in FIG. 4A. In the figure, the assay capture phasehas antibody to viral antigen attached to a solid phase. The viralparticle (antigen) can be captured by the solid phase and adetection-reagent, e.g., Alkaline-phosphatase labeled antibody to viralantigen, can be used to detect the host antibodies. This assay isconfigured as an antigen assay. Antibody is detected by spiking viralantigen to the sample, e.g., bodily fluid such as blood or plasma, andcomparing the assay response with and without added antigen. Anti-viralantibodies can be measured by adding (spiking) a known, fixed amount ofvirus or viral antigen to the patient sample. Following incubation, thespiked sample is used in an assay for viral antigen. If antibodies arepresent, the assay will exhibit reduction in measured antigen (low spikerecovery). The sample dilution or the level of the spiked antigen can betitrated to give a quantitative value for the antibody. When antibody toviral antigen is present, there is little or no signal generated in theabsence of added antigen. There is a reduced (or zero) response whenantigen is added compared with the response to antigen-negative controlsamples which were spiked with antigen. In other words, the antigen“spike recovery” is low or zero. The amount of antibody can be deducedfrom the spike recovery if it is more than zero. Antibody in the samplecan also be titered by using increasing antigen spikes until an assayresponse is obtained. One of skill will appreciate that the assays canbe adapted to detect host antibody to other virus strains, e.g., H5N1.The method can also be adapted to detect host antibodies to anyappropriate antigen, e.g., to other microbial insults.

Another configuration to detect host antibodies to influenza viralparticles is shown schematically in FIG. 4B. This is a direct detectionmethod. In this embodiment, the assay capture phase has viral antigenattached to a solid phase and uses a detection-reagent comprised ofAlkaline-phosphatase labeled antibody to human immunoglobulin. Asdescribed herein, the ideotype of the host antibodies can determinewhether the host is naive to the antigen (IgM antibodies are found) orhas had prior exposure (IgG or IgA antibodies are found). By use ofantibodies specific to immunoglobulin species, e.g., IgM, IgG, IgA,etc.), the type of antibody can be determined. The assay involves: 1)incubating sample with a capture surface to which is bound virus and/orviral antigen; 2) washing the surfact to remove unbound IgG, then 3)incubating with an enzyme-labeled anti-human immunoglobulin specific foreither IgG of IgM; 4) washing to remove unbound enzyme-labeled antibody;and 4), incubating with substrate. FIG. 4B shows the assay status afterthe fourth step.

The FS systems are used to monitor the analytes and other individualparameters (blood pressure, temperature, weight, etc.) over time. Insome embodiments, tests are performed on an individual on a setschedule, e.g., one or more assays might be performed at least every 1h, 2 h, 3 h, 4 h, 5 h, 6 h, 7 h, 8 h, 9 h, 10 h, 11 h, 12 h, 13 h, 14 h,15 h, 16 h, 17 h, 18 h, 19 h, 20 h, 21 h, 22 h, 23 h, 24 h, 36 h, 2days, 3 days, 4 days, 5 days, 6 days, 1 week, 10 days, 2 weeks, 3 weeks,4 weeks, 1 month, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 2 months, 9 weeks,10 weeks, 11 weeks, 12 weeks, 3 months, 4 months, 5 months, 6 months, 7months, 8 months, 9 months, 10 months, 11 months, or at least everyyear. The frequency of testing can vary between individuals and betweendifferent diseases. For example, those considered to be at risk, e.g.,school children, the elderly, health care workers and physicians, can betested more frequently. In some embodiments, the OS directs thefrequency of assays. For example, the OS may identify those at risk atschedule more frequent testing. Testing can also be scheduled inreal-time or semi-real time. For example, once an index case isidentified, other individuals in social contact with the index casemight be tested immediately and more frequently thereafter. In someembodiments, test frequency in increased in a hotspot with increasedrisk. In some embodiments, test frequency is reduced as risk is abated,thereby conserving resources.

As noted, a variety of field devices can be used with the systems andmethods of the invention. The OS can direct an optimal deployment of theFS devices. In some embodiments, the types of assays are adjusted overtime as the threat changes, e.g., to monitor different analytes. In someembodiments, the sample type or types are adjusted over time as thethreat changes. In addition, viral nucleic acid has been detected inblood using PCR techniques, e.g., real-time PCR. In some embodiments,multi-sample type cartridges as described herein are used. Thesecartridges enable sample processing and analysis of a limited number ofanalytes in more than one sample type, e.g., using one or more of blood,concentrated red cells, sputum, saliva, nasal lavage, or other bodilyfluid. In some embodiments, multi-analyte cartridges as described hereinare used. These cartridges can perform analysis of many analytes on asingle sample type. Both types of cartridges can be used in a givensetting as deemed optimal in a given setting.

The deployed FS systems are used to test the selected sample types usingthe selected assays, and the results are reported back to the OS system,as described herein. In evaluating individuals for possible fluinfection, it is advantageous to make a series of measurements overtime. Based on early measurements, the ideal analyte set may be changedto optimize the information gathered by the assay system. Use of suchlongitudinal measurements permits computation of trends in analytelevels indicating trends in the disease processes. In some embodiments,the longitudinal measurements of the invention take account of dynamicdata from particular individuals along with population informationgathered in previous epidemics. In some embodiments, the models alsoadjust for data from cohorts of subjects exposed to a current epidemic.

The OS monitors the incoming data for incidence of infection, andprovides assessment and containment recommendations when an infection isencountered. When an infection is observed, appropriate parties arenotified, e.g., individuals, social contacts thereof, health-careworkers, and government officials. In some embodiments, the course ofaction recommended by the OS is used to contain the spread of the virus.In some embodiments, the course of action includes providing therapeutictreatment to an infected individual. In some embodiments, prophylactictreatment is administered to those in contact with the infectedindividual. This might include vaccination. In some embodiments,depending on the severity of the outbreak, infected individuals may bequarantined. Those having contact with the infected individual can bequarantined as well.

The FS and OS continue to monitor throughout, and continuously updatesthe OS database with the incoming information. In some embodiments, theOS adjusts the recommended course of action in response to the realworld measurements. In this manner, the Health Shield of the presentinvention provides dynamic response to the detected outbreak. Once anoutbreak has been contained, the FS components of the system can berelocated to alternate hotspots, etc.

6. Monitoring Infectious Disease

It will be appreciated that the systems of the invention as describedabove can be employed to monitor the incidence of a number of infectiousdiseases in addition to influenza. For example, the HS can be deployedto monitor and prevent spread of infectious diseases in areas whereresources are limited, e.g., rural or remote areas, or developingcountries. In some embodiments, the HS is used to monitor AcquiredImmune Deficiency Syndrome (AIDS), tuberculosis (TB), and/or malaria.AIDS is a disease of the human immune system caused by the humanimmunodeficiency virus (HIV). HIV is transmitted through direct contactof a mucous membrane or the bloodstream with a bodily fluid containingHIV, such as blood, semen, vaginal fluid, preseminal fluid, and breastmilk. The disease is also spread due to sharing of infected syringesused to inject illicit drugs. AIDS progressively reduces theeffectiveness of the immune system and leaves individuals susceptible toopportunistic infections and tumors. This weakening of the immune systemexacerbates the risks of TB and malaria. Tuberculosis is a common andoften deadly infectious disease caused by mycobacteria, e.g.,Mycobacterium tuberculosis. Tuberculosis resides mostly in the lungs,and is spread through the air, when infected individuals cough, sneeze,or spit. Malaria is a vector-borne infectious disease caused byprotozoan parasites, and is spread by the bite of an infective femaleAnopheles mosquito. AIDS, TB and malaria each kill over a million peoplea year, mostly in developing countries. Treatments are available forthese infectious agents, but the cost of treatment varies widely. TB andmalaria treatments are relatively inexpensive but AIDS treatments can becostly. Drug-resistance can be an issue for all of these pathogens.

In some embodiments, the HS system is deployed to monitor and limit thespread of infectious diseases including AIDS, TB and malaria. In someembodiments, this configuration of the Health Shield is deployed indeveloping countries. The general infrastructure can be manner similarto that described above for influenza. The data entered into the modelcan include pharmacokinetic and pharmacodynamic (PK/PD) data for thevarious drugs and drug combination administered for the diseases. Assaysfor drug resistance can also be included in the FS systems. The systemmay also gather information about the drug therapy compliance of theindividuals. The system can thereby estimate the optimal treatmentregimen for each individual. Given an individual's profile, one personmay be treated with drug regimen aimed at aggressively curing or haltingdisease progression. Another individual may be assigned a treatment thatis less optimal for achieving rapid cure, but will have a highercompliance rate (e.g., fewer treatments, e.g., fewer pills per day) andultimately achieve better long term results for that individual.

The FS systems can be located in developing hot spots. Hot spots caninclude, e.g., areas with a greater amount of infective mosquitoes, orareas wherein the individuals have lesser ability to protect themselvesfrom mosquito bites. In some embodiments, central testing zones may beconstructed within the hotspots. In some embodiments, individualswithout access to power may have blood samples taken and/or analyzed ina central lab setting that has the necessary resources. These labs canbe located at or near the hotspots. In some embodiments, the centrallabs are contained in mobile units that can be moved to the location ofthe individuals.

The HS systems of the invention can be configured to provide strategiesand recommendations for controlling the spread of the disease.Individuals and organizations in a hotspot or monitored area can beeducated about the disease, e.g., causes, treatments, and methods toavoid spread. In some embodiments, the OS models suggest activeprotective measures. For example, if the system identifies an emerginghotspot for TB, extra mosquito nets, bug sprays, insecticides, oranti-pesticides can be deployed to that area. Vaccinations orprophylactic treatments can also be administered. In some embodiments,the model predicts areas where the infection is most likely to spread,thus allowing early or preemptive vaccination in those areas to preventdisease. Infected individuals or groups of individuals can be placedunder supervision or quarantined. In some embodiments, individuals arequarantined within their home, a hospital or other care facility.Additionally, the contacts of an infected individual, e.g., friends,family and co-workers, can be quarantined or placed under closemonitoring or surveillance. In some embodiments, the HS systemidentifies carriers, i.e., individuals who carry a disease but are notsymptomatic. For example, about 80% of the population of Africa testspositive for tuberculosis. In some embodiments, steps are taken toreduce spread by carriers. For example, the carriers can be treated,educated about methods to reduce spread, e.g., avoiding exchange ofbodily fluids or hygienic methods, or quarantined as appropriate. The OSsystem can provide estimates of the overall benefits and cost-benefitanalysis of various actions to be taken.

The assays of the FS systems can be designed to measure analytesspecific to the disease or disease being monitored. Non-limitingexamples of analytes measured when monitoring AIDS, TB and malariainclude HIV virus, HIV viral RNA, IgM antibodies to HIV, IgG antibodiesto HIV, CD4, CD8, and/or drug treatments. Non-limiting examples ofanalytes measured when monitoring TB include TB antigens, anti-TBantibodies, mycobacterium antibodies and interferon gamma, which canrise upon infection. Non-limiting examples of analytes measured whenmonitoring malaria include malarial antigens and anti-malariaantibodies. Various actions that can be taken when detecting AIDSanalytes include without limitation those actions listed in Table 6.

TABLE 6 Analyte and action matrix for AIDS Community Analyte oranalytical indication Interpretation Action action Viral RNA Currentinfection Treat Council contacts Low helper T-cell [CD4] (#) Currentinfection Treat Council contacts Low CD4/CD8 ratio Current infectionTreat Council contacts IgM Antibody to virus Recent infection Initiatetreatment Contact tracing IgG Antibody to virus Established TreatCouncil infection contacts Protective antibody Subject for research NoneAntibody to CMV Risk of blindness Monitor/Treat None Antibody to HerpesVirus Risk of severe Monitor/Treat None herpes Viral resistance to drugMutation of virus Change drug Council contacts Viral resistance to drugMutation of virus Change Council combinations combination contacts Druglevel not optimal Adjust None Increase in viral RNA level Viral breakoutTreat aggressively None Decrease in CD4 Viral breakout Treataggressively None Decrease in CD4/CD8 Viral breakout Treat aggressivelyNone

In some embodiments, the systems of the invention are used to monitorchronic, incurable infectious diseases. Such diseases are over spread bycontact with infected blood and other bodily fluids. AIDS is currentlyincurable but individuals with HIV can sometimes live for decadesthrough the use of antiviral treatments. Transmission can be reduced byover 80% through the proper use of condoms, restriction of sexualpartners and abstinence. Hepatitis B and C are chronic liver diseasescaused by infection with hepatitis B and C virus, respectively. TheHealth Shield of the present invention can be used to monitor the healthstatus of those with hepatitis, in a similar manner to other infectiousdisease as described herein. For example, containment methods at hotspots can be implemented, e.g., education and distribution of condomscan be used to halt the spread of Hepatitis C, which can be spreadthrough sexual contact. At the individual level, infected individualscan be assigned appropriate education and therapy or interventions ifthe condition worsens. For example, liver damage in the late stages ofhepatitis can be made worse by alcohol abuse. Infected individuals canbe educated about such adverse effects of alcohol. Non-limiting examplesof analytes measured when monitoring hepatitis include hepatitis B viralantigens, hepatitis C viral antigens, hepatitis B viral DNA, hepatitis Cviral DNA, anti-hepatitis B surface antigen antibodies, anti-hepatitis Csurface antigen antibodies, anti-hepatitis B core protein antibodies,anti-hepatitis C core protein antigen antibodies. Non-limiting examplesof analytes measured when monitoring liver function include aspartatetransaminase (AST) or alanine transaminase (ALT). The AST/ALT ratio issometimes useful in differentiating between causes of liver damage whenliver enzymes are elevated. For example, a ratio greater than 2.0 ismore likely to be associated with alcoholic hepatitis whereas a ratioless than 1.0 is more likely to be associated with viral hepatitis.

Those of skill in the art will appreciate that the Health Shield systemcan be configured and adapted for the monitoring and containment of anynumber of infectious agents using similar approaches as describedherein. The present invention includes monitoring of the followingnon-limiting infectious agents and analytes thereof: Adenovirus,Bordella pertussis, Chlamydia pneumoiea, Chlamydia trachomatis, CholeraToxin, Cholera Toxin β, Campylobacter jejuni, Cytomegalovirus, DiptheriaToxin, Epstein-Barr NA, Epstein-Barr EA, Epstein-Barr VCA, HelicobacterPylori, Hepatitis B virus (HBV) Core, Hepatitis B virus (HBV) Envelope,Hepatitis B virus (HBV) Surface (Ay), Hepatitis C virus (HCV) Core,Hepatitis C virus (HCV) NS3, Hepatitis C virus (HCV) NS4, Hepatitis Cvirus (HCV) NS5, Hepatitis A, Hepatitis D, Hepatitis E virus (HEV) orf23 KD, Hepatitis E virus (HEV) orf2 6 KD, Hepatitis E virus (HEV) orf3 3KD, Human immunodeficiency virus (HIV)-1 p24, Human immunodeficiencyvirus (HIV)-1 gp41, Human immunodeficiency virus (HIV)-1 gp120, Humanpapilloma virus (HPV), Herpes simplex virus HSV-1/2, Herpes simplexvirus HSV-1 gD, Herpes simplex virus HSV-2 gG, Human T-cell leukemiavirus (HTLV)-1/2, Influenza A, Influenza A H3N2, Influenza B, Leishmaniadonovani, Lyme disease, Mumps, M. pneumoniae, M. tuberculosis,Parainfluenza 1, Parainfluenza 2, Parainfluenza 3, Polio Virus,Respiratory syncytial virus (RSV), Rubella, Rubeola, Streptolysin O,Tetanus Toxin, T. pallidum 15 kd, T. pallidum p47, T. cruzi, Toxoplasma,and Varicella Zoster.

7. Monitoring Chronic Disease and Treatment Efficacy

In addition to monitoring infectious disease, the Health Shield makes itpossible to understand an individual's disease trajectory and his/herresponse to therapy. Given both the inherent genetic variance embeddedin the human species and variability of an individual's environment, theability to monitor and track the most informative pathophysiologicfactors in a disease process allows us to determine whether a therapy iseffective. Such monitoring can help make sure that health care dollarsare spent on treatments and drugs that work. With traditional laboratorysystems, up to 50% of individuals fail to comply with prescriptions forlaboratory testing and as many as 60% of therapeutic prescriptions donot have the intended effects. The HS provides greater compliance viahome deployment and greater drug effectiveness by real-time monitoringof efficacy. Because the HS provides for point-of-care testing, it helpsfacilitate compliance with lab testing orders.

In some embodiments, the integrated technologies of the invention areused to manage chronic diseases like congestive heart failure. Suchmonitoring can help improve quality of life and avoid costlyhospitalizations through pre-emptive action. For diabetic individuals,the systems can provide automated counseling that help coordinate andmanage life style changes and reverses the progression of the diseaseand prevents (and predicts) complications. By improving outcomes andallowing for earlier interventions, significant healthcare savings canbe achieved. In some embodiments, the same systems can be used tomonitor the interactions between drugs for chronic disease patientstaking multiple therapies. This ability not only prevents adverse drugreactions and reduces the costs of the associated complications but alsoallows potentially life saving drugs to be used more widely in chronicdisease populations.

Diabetes mellitus (diabetes) is a condition in which the body eitherfails to properly produce or respond to insulin, a hormone produced inthe pancreas that enables cells to absorb glucose in order to turn itinto energy. In diabetes, the body either fails to properly respond toinsulin, does not make enough insulin, or both. This causes glucose toaccumulate in the blood, leading to various complications. Acutecomplications including hypoglycemia, diabetic ketoacidosis, ornonketotic hyperosmolar coma may occur if the disease is not adequatelycontrolled. Serious long-term complications include cardiovasculardisease, chronic renal failure, retinal damage and blindness, severaltypes of nerve damage, and microvascular damage, which may causeerectile dysfunction and poor wound healing. Poor healing of wounds,particularly of the feet, can lead to gangrene, and possibly amputation.In type 1 diabetes, or juvenile diabetes, the body fails to produceinsulin. Presently almost all persons with type 1 diabetes must takeinsulin injections. Type 2 diabetes, also known as adult-onset orlate-onset diabetes, results from insulin resistance, a condition inwhich cells fail to use insulin properly, sometimes combined withrelative insulin deficiency. About 90% of Americans who are diagnosedwith diabetes have type 2 diabetes. Many people destined to develop type2 diabetes spend many years in a state of pre-diabetes, a condition thatoccurs when a person's blood glucose levels are higher than normal butnot high enough for a diagnosis of type 2 diabetes. As of 2009 there are57 million Americans who have pre-diabetes.

Pre-diabetes has been termed “America's largest healthcare epidemic.”Handelsman, Yehuda, Md. A Doctor's Diagnosis: Prediabetes. Power ofPrevention, Vol 1, Issue 2, 2009. High sugar and high fat diets arecausing earlier onset of obesity and diabetes, especially in wealthycountries. Young people consume a diet high in sugar and fat and becomeobese, which can in turn progress to serious diseases and disorders,including but not limited to prediabetes, diabetes, heart disease. Inmany environments, easy access to carbonated beverages containing highlevels of sugar and to fat-rich fast foods promotes this process.

The HS system of the invention can be used to aid response to the spreadof diabetes. In some embodiments, the system is used to identifyindividuals at high risk. In some embodiments, the system can identifylocations, e.g., geographic locations, communities, school systems orschools, where the risk of progression to disease is highest. In anon-limiting example, consider the HS deployed within a school. The FSsystem would be deployed to the school in a manner similar to thatdescribed above for infectious diseases. In some embodiments, schoolemployees, e.g., a school nurse, could administer assays to all studentsor to a subset of students, e.g., at risk students. The testing could beperformed at regular intervals, e.g., at least once a school year, atleast once a semester, at least once a quarter, at least monthly, atleast every three weeks, at least every two weeks, or at least weekly.In some embodiments, subsets of students could be tested at differentintervals. For example, the entire student body might be tested at afirst frequency, and a subset of the student body, e.g., thoseidentified at risk from various factors, e.g., obesity or previous testresults, could be monitored at a second frequency. In a non-limitingexample, the first frequency might be at least once a school year andthe second frequency might be at least once a semester, at least once aquarter, or at least monthly. Any similar scheme where those at risk aretested more frequently can be used.

The FS systems deployed in the schools can be used to monitor a varietyof analytes which are indicative of risk or disease, e.g., hormonelevels and glucose levels. In some embodiments, such analytes aremeasured in blood. Non-limiting examples of appropriate analytes thatcan be measured by the FS systems include glucose, hemoglobin A1c,insulin, glucagon, glucagon-like peptide 1 (GLP-1), the insulinprecursor peptide-C, leptin, adiponectin, cholesterol, HDL cholesterol,LDL cholesterol and triglycerides. Other physiological data, e.g., bodymass, can also be entered into the system for the OS component of the HSto calculate individual and group risks. The system can also monitordrug therapy, by entering a regimen into an individual's health profile,or by directly detecting drug levels with the FS. In some embodiments,the system monitors the progression of any or all of these variablesover time.

When the HS identifies an individual, e.g., a student, or a population,e.g., a student body, having or at risk of developing prediabetes ordiabetes, the system can recommend a course of action. In the case of apopulation, the system may issue a warning and/or recommend action ifthe population incidence or risk increases above a threshold level. Insome embodiments, the course of action comprises counseling toindividuals, care takers, or other individuals who can influence anindividual's lifestyle to mitigate disease or risk thereof. For example,parents or school officials may be notified. The system can alsorecommend therapies or interventions, including exercise, weight loss,altered eating habits, etc. For a population, a recommendation mightinclude population control measures, including without limitationremoval of sugary soft drinks from a school's premises, healthiercafeteria menus, and improved physical education.

Susceptibility to Type II diabetes is not only is this due to poorlifestyle choices but is affected by other factors, e.g., geneticfactors. In the United States, such variation, e.g. in the NativeAmerican population and those with significant indigenous ancestry, suchas the Hispanic population, are potentially at elevated risk.Environmental factors are also potential factors. The OS model can beextended to take into account additional factors, including withoutlimitation genetic and environmental factors. For example, the model canbe configured to include adaptive sampling based on non-assay riskmeasures. Such risk measures include without limitation body weight,medical history, blood pressure, family history, activity level, geneticvariability, and alcohol use. The model can also be configured toadaptive sampling based on FS assay data in conjunction with geographic,family, demographic, employment, health care provider, and other data.Similarly, the system can model adaptive therapeutic treatment based onoutcomes for the individual and for a population that the analyticssystem determines to be similar for the variables that best indicaterisk. The system can also incorporate visualization that assists adoctor in explaining and clarifying to the user their risk factors, andappropriate actions to mitigate risk, e.g., therapeutic and/orprophylactic treatments and/or interventions, weight loss, dietarychanges, exercise and other lifestyle changes. Such visualization mightinclude, e.g., a decision tree or heat map. In some embodiments, thevisualization shows cumulative risk from additive factors. An exemplaryuse of a decision tree for diabetes is presented in Example 4. Each ofthese approaches can be applied to the model for diabetes and otherchronic or infectious diseases.

In another embodiment, the point-of-care and real-time monitoringcapabilities of the HS can be used to improve the efficiency of clinicaltrials. The time-savings impact of the Health Shield has been quantifiednext to conventional testing and data analytics by pharmaceuticalcompanies. Modeling studies show that the HS can reduce the clinicaltrials process by potentially a number of years and save $100 Ms perprogram. In addition, the data generated can provide better success andoutcomes for the drugs monitored by defining patient populations andidentifying possible adverse events in a predictive manner.

In a separate embodiment, a method of monitoring more than onepharmacological parameter useful for assessing efficacy and/or toxicityof a therapeutic agent is provided. For example, a therapeutic agent caninclude any substance that has therapeutic utility and/or potential.Such substances include but are not limited to biological or chemicalcompounds such as simple or complex organic or inorganic molecules,peptides, proteins (e.g. antibodies) or a polynucleotides (e.g.anti-sense). A vast array of compounds can be synthesized, for examplepolymers, such as polypeptides and polynucleotides, and syntheticorganic compounds based on various core structures, and these can alsobe included as therapeutic agents. In addition, various natural sourcescan provide compounds for therapeutic use, such as plant or animalextracts, and the like. It should be understood, although not alwaysexplicitly stated that the agent is used alone or in combination withanother agent, having the same or different biological activity as theagents identified by the inventive screen. The agents and methods alsoare intended to be combined with other therapies. For example, smallmolecule drugs are often measured by mass-spectrometry which can beimprecise. ELISA (antibody-based) assays can be much more accurate andprecise.

Physiological parameters according to the present invention includewithout limitation parameters such as temperature, heart rate/pulse,blood pressure, and respiratory rate. Pharmacodynamic parameters includeconcentrations of biomarkers such as proteins, nucleic acids, cells, andcell markers. Biomarkers could be indicative of disease or could be aresult of the action of a drug. Pharmacokinetic (PK) parametersaccording to the present invention include without limitation drug anddrug metabolite concentration. Identifying and quantifying the PKparameters in real time from a sample volume is extremely desirable forproper safety and efficacy of drugs. If the drug and metaboliteconcentrations are outside a desired range and/or unexpected metabolitesare generated due to an unexpected reaction to the drug, immediateaction may be necessary to ensure the safety of the patient. Similarly,if any of the pharmacodynamic (PD) parameters fall outside the desiredrange during a treatment regime, immediate action may have to be takenas well.

Being able to monitor the rate of change of an analyte concentration orPD or PK parameters over a period of time in a single subject, orperforming trend analysis on the concentration, PD, or PK parameters,whether they are concentrations of drugs or their metabolites, can helpprevent potentially dangerous situations. For example, if glucose werethe analyte of interest, the concentration of glucose in a sample at agiven time as well as the rate of change of the glucose concentrationover a given period of time could be highly useful in predicting andavoiding, for example, hypoglycemic events. Such trend analysis haswidespread beneficial implications in drug dosing regimen. When multipledrugs and their metabolites are concerned, the ability to spot a trendand take proactive measures is often desirable.

A number of other diseases and conditions can be monitored using the HSsystem and methods described herein. For example, the system can be usedto monitor and control spread of a microorganism, virus, orChlamydiaceae. Exemplary microorganisms include but are not limited tobacteria, viruses, fungi and protozoa. Analytes that can be detected bythe subject method also include blood-born pathogens selected from anon-limiting group that consists of Staphylococcus epidermidis,Escherichia coli, methicillin-resistant Staphylococcus aureus (MSRA),Staphylococcus aureus, Staphylococcus hominis, Enterococcus faecalis,Pseudomonas aeruginosa, Staphylococcus capitis, Staphylococcus warneri,Klebsiella pneumoniae, Haemophilus influenzae, Staphylococcus simulans,Streptococcus pneumoniae and Candida albicans.

Other microorganisms that can be detected by the subject method alsoencompass a variety of sexually transmitted diseases selected from thefollowing: gonorrhea (Neisseria gorrhoeae), syphilis (Treponenapallidum), clamydia (Clamyda tracomitis), nongonococcal urethritis(Ureaplasm urealyticum), yeast infection (Candida albicans), chancroid(Haemophilus ducreyi), trichomoniasis (Trichomonas vaginalis), genitalherpes (HSV type I & II), HIV I, HIV II and hepatitis A, B, C, G, aswell as hepatitis caused by TTV.

Additional microorganisms that can be detected by the subject methodsencompass a diversity of respiratory pathogens including but not limitedto Pseudomonas aeruginosa, methicillin-resistant Staphlococccus aureus(MSRA), Klebsiella pneumoniae, Haemophilis influenzae, Staphlococcusaureus, Stenotrophomonas maltophilia, Haemophilis parainfluenzae,Escherichia coli, Enterococcus faecalis, Serratia marcescens,Haemophilis parahaemolyticus, Enterococcus cloacae, Candida albicans,Moraxiella catarrhalis, Streptococcus pneumoniae, Citrobacter freundii,Enterococcus faecium, Klebsella oxytoca, Pseudomonas fluorscens,Neiseria meningitidis, Streptococcus pyogenes, Pneumocystis carinii,Klebsella pneumoniae Legionella pneumophila, Mycoplasma pneumoniae, andMycobacterium tuberculosis.

Any number of biomarkers can be detected in a deployed Health Shield.Listed below are additional exemplary markers according to the presentinvention: Theophylline, CRP, CKMB, PSA, Myoglobin, CA125, Progesterone,TxB2,6-keto-PGF-1-alpha, and Theophylline, Estradiol, Lutenizinghormone, Triglycerides, Tryptase, Low density lipoprotein Cholesterol,High density lipoprotein Cholesterol, Cholesterol, IGFR.

Exemplary liver markers include without limitation LDH, (LD5), (ALT),Arginase 1 (liver type), Alpha-fetoprotein (AFP), Alkaline phosphatase,Alanine aminotransferase, Lactate dehydrogenase, and Bilirubin.

Exemplary kidney markers include without limitation TNFa Receptor,Cystatin C, Lipocalin-type urinary prostaglandin D, synthatase (LPGDS),Hepatocyte growth factor receptor, Polycystin 2, Polycystin 1,Fibrocystin, Uromodulin, Alanine, aminopeptidase,N-acetyl-B-D-glucosaminidase, Albumin, and Retinol-binding protein(RBP).

Exemplary heart markers include without limitation Troponin I (TnI),Troponin T (TnT), CK, CKMB, Myoglobin, Fatty acid binding protein(FABP), CRP, D-dimer, S-100 protein, BNP, NT-proBNP, PAPP-A,Myeloperoxidase (MPO), Glycogen phosphorylase isoenzyme BB (GPBB),Thrombin Activatable Fibrinolysis Inhibitor (TAFI), Fibrinogen, Ischemiamodified albumin (IMA), Cardiotrophin-1, and MLC-I (Myosin LightChain-I).

Exemplary pancreatic markers include without limitation Amylase,Pancreatitis-Associated protein (PAP-1), and Regeneratein proteins(REG).

Exemplary muscle tissue markers include without limitation Myostatin.

Exemplary blood markers include without limitation Erythopoeitin (EPO).

Exemplary bone markers include without limitation, Cross-linkedN-telopeptides of bone type I collagen (NTx) Carboxyterminalcross-linking telopeptide of bone collagen, Lysyl-pyridinoline(deoxypyridinoline), Pyridinoline, Tartrate-resistant acid phosphatase,Procollagen type I C propeptide, Procollagen type 1 N propeptide,Osteocalcin (bone gla-protein), Alkaline phosphatase, Cathepsin K, COMP(Cartillage Oligimeric Matrix Protein), Osteocrin, Osteoprotegerin(OPG), RANKL, sRANK, TRAP 5 (TRACP 5), Osteoblast Specific Factor 1(OSF-1, Pleiotrophin), Soluble cell adhesion molecules, sTfR, sCD4,sCD8, sCD44, and Osteoblast Specific Factor 2 (OSF-2, Periostin).

In some embodiments markers according to the present invention aredisease specific. Exemplary cancer markers include without limitationPSA (total prostate specific antigen), Creatinine, Prostatic acidphosphatase, PSA complexes, Prostrate-specific gene-1, CA 12-5,Carcinoembryonic Antigen (CEA), Alpha feto protein (AFP), hCG (Humanchorionic gonadotropin), Inhibin, CAA Ovarian C1824, CA 27.29, CA 15-3,CAA Breast C1924, Her-2, Pancreatic, CA 19-9, CAA pancreatic,Neuron-specific enolase, Angiostatin DcR3 (Soluble decoy receptor 3),Endostatin, Ep-CAM (MK-1), Free Immunoglobulin Light Chain Kappa, FreeImmunoglobulin Light Chain Lambda, Herstatin, Chromogranin A,Adrenomedullin, Integrin, Epidermal growth factor receptor, Epidermalgrowth factor receptor-Tyrosine kinase, Pro-adrenomedullin N-terminal 20peptide, Vascular endothelial growth factor, Vascular endothelial growthfactor receptor, Stem cell factor receptor, c-kit/KDR, KDR, and Midkine.

Exemplary infectious disease conditions include without limitation:Viremia, Bacteremia, Sepsis, and markers: PMN Elastase, PMNelastase/α1-PI complex, Surfactant Protein D (SP-D), HBVc antigen, HBVsantigen, Anti-HBVc, Anti-HIV, T-supressor cell antigen, T-cell antigenratio, T-helper cell antigen, Anti-HCV, Pyrogens, p24 antigen,Muramyl-dipeptide.

Exemplary diabetes markers include without limitation C-Peptide,Hemoglobin A1c, Glycated albumin, Advanced glycosylation end products(AGEs), 1,5-anhydroglucitol, Gastric Inhibitory Polypeptide, Glucose,Hemoglobin, ANGPTL3 and 4.

Exemplary inflammation markers include without limitation TNF-α, IL-6,IL1β, Rheumatoid factor (RF), Antinuclear Antibody (ANA), acute phasemarkers including C-reactive protein (CRP), Clara Cell Protein(Uteroglobin).

Exemplary allergy markers include without limitation Total IgE andSpecific IgE.

Exemplary autism markers include without limitation Ceruloplasmin,Metalothioneine, Zinc, Copper, B6, B12, Glutathione, Alkalinephosphatase, and Activation of apo-alkaline phosphatase.

Exemplary coagulation disorders markers include without limitationb-Thromboglobulin, Platelet factor 4, Von Willebrand factor.

In some embodiments a marker may be therapy specific. COX inhibitorsinclude without limitation TxB2 (Cox-1), 6-keto-PGF-1-alpha (Cox 2),11-Dehydro-TxB-1a (Cox-1).

Other markers of the present include without limitation Leptin, Leptinreceptor, and Procalcitonin, Brain S100 protein, Substance P,8-Iso-PGF-2a.

Exemplary geriatric markers include without limitation, Neuron-specificenolase, GFAP, and S100B.

Exemplary markers of nutritional status include without limitationPrealbumin, Albumin, Retinol-binding protein (RBP), Transferrin,Acylation-Stimulating Protein (ASP), Adiponectin, Agouti-Related Protein(AgRP), Angiopoietin-like Protein 4 (ANGPTL4, FIAF), C-peptide, AFABP(Adipocyte Fatty Acid Binding Protein, FABP4), Acylation-StimulatingProtein (ASP), EFABP (Epidermal Fatty Acid Binding Protein, FABP5),Glicentin, Glucagon, Glucagon-Like Peptide-1, Glucagon-Like Peptide-2,Ghrelin, Insulin, Leptin, Leptin Receptor, PYY, RELMs, Resistin, andsTfR (soluble Transferrin Receptor).

Exemplary markers of Lipid metabolism include without limitationApo-lipoproteins (several), Apo-A1, Apo-B, Apo-C-CII, Apo-D, Apo-E.

Exemplary coagulation status markers include without limitation FactorI: Fibrinogen, Factor II: Prothrombin, Factor III: Tissue factor, FactorIV: Calcium, Factor V: Proaccelerin, Factor VI, Factor VII:Proconvertin, Factor VIII: Anti-hemolytic factor, Factor IX: Christmasfactor, Factor X: Stuart-Prower factor, Factor XI: Plasma thromboplastinantecedent, Factor XII: Hageman factor, Factor XIII: Fibrin-stabilizingfactor, Prekallikrein, High-molecular-weight kininogen, Protein C,Protein S, D-dimer, Tissue plasminogen activator, Plasminogen,a2-Antiplasmin, Plasminogen activator inhibitor 1 (PAI1).

Exemplary monoclonal antibodies include those for EGFR, ErbB2, andIGF1R.

Exemplary tyrosine kinase inhibitors include without limitation Abl,Kit, PDGFR, Src, ErbB2, ErbB 4, EGFR, EphB, VEGFR1-4, PDGFRb, FLt3,FGFR, PKC, Met, Tie2, RAF, and TrkA.

Exemplary Serine/Threoline Kinas Inhibitors include without limitationAKT, Aurora A/B/B, CDK, CDK (pan), CDK1-2, VEGFR2, PDGFRb, CDK4/6,MEK1-2, mTOR, and PKC-beta.

GPCR targets include without limitation Histamine Receptors, SerotoninReceptors, Angiotensin Receptors, Adrenoreceptors, MuscarinicAcetylcholine Receptors, GnRH Receptors, Dopamine Receptors,Prostaglandin Receptors, and ADP Receptors.

Because the HS comprises a series of integrated technologies that can bequickly adapted to perform additional assays, the system offers acustomizable technology package distinct from other systems presentlyavailable. For example, systems that focus on a specifictechnology/application will have difficulty being broadly applied toimprove outcomes and reduce healthcare expenditures across all diseases.

8. Field System Cartridge Systems

(a) Field System Devices

Customized cartridge devices for use with the FS of the invention aredescribed in U.S. patent application Ser. No. 11/389,409, filed Mar. 24,2006 and entitled “POINT-OF-CARE-FLUIDIC SYSTEMS AND USES THEREOF,” U.S.patent application Ser. No. 11/746,535, filed May 9, 2007 and entitled“REAL-TIME DETECTION OF INFLUENZA VIRUS,” and U.S. patent applicationSer. No. 12/244,723, filed Oct. 2, 2008 and entitled “MODULARPOINT-OF-CARE DEVICES, SYSTEMS, AND USES THEREOF.” Further details areprovided herein.

In one embodiment, a FS device for use with the invention comprises adevice for automated detection of an analyte in a bodily fluid samplecomprises an array of addressable assay units configured to run achemical reaction that yields a detectable signal indicative of thepresence or absence of the analyte. In some embodiments, the devicefurther an array of addressable reagent units, each of which isaddressed to correspond to one or more addressable assay units in saiddevice, such that individual reagent units can be calibrated inreference to the corresponding assay unit(s) before the arrays areassembled on the device. In some embodiments, at least one of the assayunits and at least one of the reagent units are movable relative to eachother within the device such that reagents for running the chemicalreaction are automatically brought to contact with the bodily fluidsample in the assay unit. The array of assay units or reagent units canbe addressed according to the chemical reaction to be run by theconfigured assay unit.

In one embodiment, the device is self-contained and comprises allreagents, liquid- and solid-phase reagents, required to perform aplurality of assays in parallel. Where desired, the device is configuredto perform at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100,200, 500, 1000 or more assays. One or more control assays can also beincorporated into the device to be performed in parallel if desired.

The assays can be quantitative immunoassays and can be conducted in ashort period of time. Other assay type can be performed with a device ofthe invention including, but not limited to, measurements of nucleicacid sequences and measurements of metabolytes, such as cholesterol andenzymes such as alanine aminotransferase. In some embodiments, the assayis completed in no more than one hour, preferably less than 30, 15, 10,or 5 minutes. In other embodiments, the assay is performed in less than5 minutes. The duration of assay detection can be adjusted accordinglyto the type of assay that is to be carried out with a device of theinvention. For example, if needed for higher sensitivity, an assay canbe incubated for more than one hour or up to more than one day. In someexamples, assays that require a long duration may be more practical inother POC applications, such as home use, than in a clinical POCsetting.

Any bodily fluids suspected to contain an analyte of interest can beused in conjunction with the system or devices of the invention.Commonly employed bodily fluids include but are not limited to blood,serum, saliva, urine, gastric and digestive fluid, tears, stool, semen,vaginal fluid, interstitial fluids derived from tumorous tissue, andcerebrospinal fluid.

A bodily fluid may be drawn from a patient and provided to a device in avariety of ways, including but not limited to, lancing, injection, orpipetting. As used herein, the terms subject and patient are usedinterchangeably herein, and refer to a vertebrate, preferably a mammal,more preferably a human. Mammals include, but are not limited to,murines, simians, humans, farm animals, sport animals, and pets. In oneembodiment, a lancet punctures the skin and a sample is collected using,e.g., gravity, capillary action, aspiration, or vacuum force. The lancetmay be part of the device, or part of a system, or a stand alonecomponent. Where needed, the lancet may be activated by a variety ofmechanical, electrical, electromechanical, or any other known activationmechanism or any combination of such methods. In another embodimentwhere no active mechanism is required, a patient can simply provide abodily fluid to the device, as for example, could occur with a salivasample. The collected fluid can be placed in the sample collection unitwithin the device. In yet another embodiment, the device comprises atleast one microneedle which punctures the skin.

The volume of bodily fluid to be used with a device is generally lessthan about 500 microliters, typically between about 1 to 100microliters. Where desired, a sample of 1 to 50 microliters, 1 to 40microliters, 1 to 30 microliters, 1 to 10 microliters or even 1 to 3microliters can be used for detecting an analyte using the device.

In an embodiment, the volume of bodily fluid used for detecting ananalyte using the subject devices or systems is one drop of fluid. Forexample, one drop of blood from a pricked finger can provide the sampleof bodily fluid to be analyzed with a device, system or method describedherein.

A sample of bodily fluid can be collected from a subject and deliveredto a device of the invention as described hereinafter.

In an embodiment, the arrays of assay and reagent units are configuredto be a set of mix-and-match components. The assay units can comprise atleast one capture surface capable of reacting with an analyte from thesample of bodily fluid. The assay unit may be a tubular tip with acapture surface within the tip. Examples of tips of the invention aredescribed herein. A reagent unit typically stores liquid or solidreagents necessary for conducting an assay that detect a give analyte.Each individual assay and reagent unit can be configured for assayfunction independently. To assemble a device, the units can be assembledin a just-in-time fashion for use in integrated cartridges.

Separate components, both liquid and solid phase, can be made and thenbe tested for performance and stored. In an embodiment, the assembly ofthe device is carried out in on-demand fashion at a manufacturinglocation. The device can be modular and include components such as ahousing that is generic for all assays, assay units, such as tips, andreagent units, such as a variety of frangible or instrument operablecontainers that encapsulate liquid reagents. In some instances, anassembled device is then tested to define and/or verify calibration (therelation of the system response to known analyte levels). Assay devicescan be assembled from a library of pre-manufactured and calibratedelements on demand. In some embodiments, fluidic pathways within adevice can be simple and obviate any chance of trapping bubbles andproviding an efficient way to wash away excess labeled reagents inreagent excess assays such as ELISAs.

A housing for a FS device of the invention can be made of polystyrene oranother moldable or machinable plastic and can have defined locations toplace assay units and reagent units. In an embodiment, the housing hasmeans for blotting tips or assay units to remove excess liquid. Themeans for blotting can be a porous membrane, such as cellulose acetate,or a piece bibulous material such as filter paper.

In some embodiments, at least one of the components of the device may beconstructed of polymeric materials. Non-limiting examples of polymericmaterials include polystyrene, polycarbonate, polypropylene,polydimethysiloxanes (PDMS), polyurethane, polyvinylchloride (PVC),polysulfone, polymethylmethacrylate (PMMA),acrylonitrile-butadiene-styrene (ABS), and glass.

The device or the subcomponents of the device may be manufactured byvariety of methods including, without limitation, stamping, injectionmolding, embossing, casting, blow molding, machining, welding,ultrasonic welding, and thermal bonding. In an embodiment, a device inmanufactured by injection molding, thermal bonding, and ultrasonicwelding. The subcomponents of the device can be affixed to each other bythermal bonding, ultrasonic welding, friction fitting (press fitting),adhesives or, in the case of certain substrates, for example, glass, orsemi-rigid and non-rigid polymeric substrates, a natural adhesionbetween the two components.

An exemplary device as described herein is illustrated in FIG. 5. Thedevice 100 is also sometimes referred to herein as a cartridge 100. Thedevice 100 comprises a housing 130 with locations to accommodate assayunits 121 and reagent units 103, 122, 124, 125. In the exemplaryembodiment of FIG. 5, assay units 121 occupy a center row of the housing130 of the device 100. The assay units 121 can optionally include atleast one calibration unit 126. In an example, the assay units 121 aresimilar to pipette tips and are referred to as assay tips 121 and thecalibration units 126 are referred to as calibration tips 126 herein,however, the assay units 121 can be of any shape and size as areaccommodated broadly by a device 100 as described herein. The assayunits 121 and calibration units 126 are exemplary assay units 121 andare described in more detail herein. The assay units 121 in FIG. 5 cancomprise a capture surface and are capable, for example, of performing achemical reaction such as nucleic acid assays and immunoassays. Theassay units 121 can be assembled into the housing according toinstructions or the assays that a user wishes to perform on a sample.

As shown in FIG. 5, the housing of the device 100 can comprise a samplecollection unit 110 configured to contain a sample. A sample, such as ablood sample, can be placed into the sample collection unit 110. Asample tip 111 (for example, a pipette tip that couples to a fluidtransfer device as described in more detail herein) can occupy anotherportion of the housing 130. When an assay is to be run the sample tip111 can distribute the sample to pretreatment reagent units orpretreatment units 103, 104, 105, 106, 107, or assay units 121.Exemplary pretreatment units 103, 104, 105, 106, 107 include but are notlimited to: mixing units 107, diluent or dilution units 103, 104, and,if the sample is a blood sample, plasma removal or retrieval units 105,106. The pretreatment units 103, 104, 105, 106, 107 can be the same typeof unit or different types of units. Other pretreatment units 103, 104,105, 106, 107 as are necessary to run a chemical reaction can beincorporated into device 100 as would be obvious to one skilled in theart with knowledge of this disclosure. The units 103, 104, 105, 106, 107can contain various amounts of reagents or diluents, flexible towhatever is needed to run the assay on the current cartridge 100.

Often, the assay units 121 can be manufactured separately from thehousing 130 and then inserted into the housing 130 with pick-and-placemethods. The assay units 121 can fit snugly into the housing 130 or canfit loosely into the housing 130. In some embodiments, the housing 130is manufactured such that it holds the reagent units 103, 122, 124, 125and/or assay units 121 snugly in place, for example during shipping ormanipulation a cartridge. Reagents units 103, 122, 124, 125 are shown inFIG. 5 that contain a conjugate reagent 122 (for example, for use withan immunoassay), a wash reagent 125 (for example, to wash said conjugatefrom capture surfaces), and a substrate 124 (for example, an enzymesubstrate). Other embodiments of the device 100 and the components inthe example in FIG. 5 are described herein. Reagent units 103, 122, 124,125 can be manufactured and filled separately from the housing 130 andthen placed into the housing 130. In this way, a cartridge 100 can bebuilt in a modular manner, therefore increasing the flexibility of thecartridge 100 to be used for a variety of assays. Reagents in a reagentunit 103, 122, 124, 125 can be chosen according to the assay to be run.Exemplary reagents and assays are described herein.

A device, such as the example shown in FIG. 5, can also comprise otherfeatures as may be needed to run a chemical reaction. For example, ifthe assay units 121 are assay tips 121 as described herein, the devicecan comprise tip touch-off pads 112 to remove excess sample or reagentfrom an assay tip 121 or a sample tip 111 after fluid transfer, forexample, by a system as described herein. The housing 130 can alsocomprise units or areas 101, 102 within the device 100 for placing aused tip or unit, for example, in order to avoid cross-contamination ofa sample tip 111 or assay unit 121. In FIG. 5, the device 100 comprisesa sample tip 111 for transferring a sample between units of the device100. The device 100 as illustrated in FIG. 5 also comprises apretreatment tip 113 for transferring a sample that has been pretreatedin a unit of the device 100 to other units of a device 100 to perform achemical reaction. For example, the sample tip 111 can be used to removea blood sample from the sample collection unit 110 and transfer theblood sample to pretreatment units 103, 104, 105, 106, 107 as described.Red cells can be removed from the blood sample in the pretreatment units103, 104, 105, 106, 107 and the pretreatment tip 113 can then be used tocollect the blood plasma from the pretreatment units 103, 104, 105, 106,107 and transfer the blood plasma to another pretreatment unit (forexample, a diluent unit) 103, 104, 105, 106, 107 and/or to at least oneassay unit 121. In an embodiment, a sample tip 111 is the samplecollection unit 110. In another embodiment, the sample collection unit110 is similar to a well and is configured to contain a sample asreceived by a user.

Assay units 121 and reagent units 103, 122, 124, 125 as shown in FIG. 5can be addressable to indicate the location of the units on thecartridge 100. For example, a column of the cartridge 100 as shown inFIG. 5 can contain an assay unit 121 to run an assay configured todetect C-reactive protein, and the column can contain correspondingreagent units 103, 122, 124, 125 for that assay in the same column,wherein the units are addressed to correspond to each other. Forexample, the addresses can be entered and stored in a computer system,and the cartridge 100 can be given a label, such as a bar code. When thebar code of the cartridge 100 is scanned for use, the computer systemcan send the addresses of the units to a system, such as those describedherein, to transfer the fluids and run a reaction according to theaddresses entered into the computer. The addresses can be part of aprotocol sent to operate the system. The addresses can be in anyconfiguration and can be altered if need be to change the protocol ofrunning an assay, which in turn can offer a change in assay protocol orsteps to a user of the cartridge that has not been typically availablein prior art POC devices. In some embodiments, the housing 130 and unitsare configured in a 6 by 8 array of units as shown in FIG. 5. The layoutof the units can be of any format, for example, rectangular arrays orrandom layouts. A cartridge 100 can comprise any number of units, forexample between 1 and about 500. In some embodiments, a cartridge 100has between 5-100 units. As an example as shown in FIG. 5, the cartridge100 has 48 units.

Two side cut-away views of the exemplary device 200 of FIG. 5 areillustrated in FIGS. 6A and 6B. A cavity can be shaped in a housing 220of a device to accommodate assay units (for example, assay tips) 201 ina vertical orientation (housing horizontal) with their bosses toward thetop of the device 200. As shown in FIG. 6, a cavity can also be shapedto accommodate a reagent unit 210, 212 or a sample collection unit ortip 202. There may be features in the housing 220 to capture the unitsprecisely and hold them securely. Such features can also be designed tooperate with a mechanism for moving the tips, such as tip pick-up anddrop-off. In another embodiment, the sample collection unit comprises abendable or breakable element that serves to protect a small collectiontube during shipment and to hold a plunger device in place within acapillary. Also shown in FIG. 6A are two exemplary embodiments ofreagent units 210, 212 as are described herein. The bottom of thehousing 220 can be configured to collect waste liquids, for example,wash reagents after use that are transferred back through a hole in thehousing 220 to the bottom. The housing 220 can comprise an absorbent padto collect waste fluids. The assay units 201 and sample units 202 can bepositioned to fit through a cavity of the housing 220 of the device 200and extend beyond an inner support structure. The reagent units 210, 212fit snugly into the housing as is shown in FIG. 6 and do not extendbeyond the inner support structure. The housing 220 and the areas inwhich the assay units 201 and reagents units 210, 212 can be held andpositioned may adapt a variety of patterns.

In some embodiments, each tip provides for a single assay and can bepaired with or corresponded to an appropriate reagent, such as requiredreagents for running the designated assay. Some tips provide for controlassay units and have known amounts of analyte bound to their capturesurfaces either in the manufacturing process or during the performanceof an assay. In case of a control assay unit, the unit is configured torun a control assay for comparison. The control assay unit may comprise,for example, a capture surface and analyte that are in a solid or liquidstate.

In many embodiments, the device holds all reagents and liquids requiredby the assay. For example, for a luminogenic ELISA assay the reagentswithin the device may include a sample diluent, capture surfaces (e.g.,three capture antibodies), a detector conjugate (for example, threeenzyme-labeled antibodies), a wash solution, and an enzyme substrate.Additional reagents can be provided as needed.

In some embodiments, reagents can be incorporated into a device toprovide for sample pretreatment. Examples of pretreatment reagentsinclude, without limitation, white cell lysis reagents, red cell lysisreagents, red cell removal reagents, reagents for liberating analytesfrom binding factors in the sample, enzymes, and detergents. Thepretreatment reagents can also be added to a diluent contained withinthe device.

An individual reagent unit can be configured to receive a movable assayunit. In some embodiments, the individual assay unit comprises an openended hollow cylindrical element comprising a capture surface and areaction cuvette. A cylindrical assay unit can be referred to as anassay tip herein. In some embodiments, the individual assay unit isconfigured to run an immunoassay. An assay unit 301 that comprises asmall tip or tubular formation is shown in FIG. 7A. In some instances,the tip 301 is configured to provide an interior cylindrical capturesurface 311 and a boss 321 capable of engaging with the housing ofdevice. In some instances, the boss 321 and the tip 301 is configured toengage with a mechanism of moving the tip 301 such as a system asdescribed herein or for example, a fluid transfer device. An assay tip301 as shown in FIG. 7A can comprise an opening 331 at the bottom of thetip. The opening 331 can be utilized for transferring fluids or reagentsin and out of an assay unit 301. In an embodiment, an assay unit 301 asdescribed is or is similar to a pipette tip with the improvement thatthe assay unit 301 comprises a capture surface 311 configured to detectan analyte in a sample.

The tip 301 can be manufactured by an injection-molded process. In anembodiment, the tip 301 is made of a clear polystyrene for use withchemiluminescence assays. As shown in FIG. 7A, an exemplary tip 301comprises a boss (shown as the larger top half of the tip 301), whichcan engage with a housing and can engage, for example, with taperedelements of a fluid transfer device and/or pipetting devices so as toform a pressure-tight seal. Also shown in FIG. 7A, the exemplary tip 301comprises a smaller cylindrical part. In many embodiments, an assaycapture surface is contained within the smaller cylindrical part. Theassay capture surface can be anywhere within the tip 301 or on theoutside of the tip 301. The surface of the tip 301 can be of manygeometries including, but not limited to, tubular, cubic, or pyramidal.In chemiluminescence and fluorescence-based assays, the tip 301 canserve as a convenient means to present the assay product to the assayoptics.

FIG. 7B demonstrates an exemplary sample collection unit 302 comprisinga sample tip 302. The sample tip 302 as shown in FIG. 7B can also beseparate from a sample collection unit 302 and used to transfer samplefrom the sample collection units to other units on a device as describedherein. The sample tip as shown in FIG. 7B comprises a boss 322 asdescribed herein to couple the tip 302 with a housing of a device and afluid transfer device. The sample tip 302 also comprises an opening 332to allow the transfer of fluids or samples in and out of the sample tip.In some embodiments, the sample tip 302 is of the same shape as an assaytip 301. In other embodiments (such as those shown in FIGS. 7A and 7B),the sample tip 302 is a different shape than the assay tip 301.

In an embodiment, one function of a tip is to enable samples and liquidreagents to be brought into contact with the capture surface of theassay unit. The movement can occur by a variety of means including, butnot limited to, capillary action, aspiration, and controlled pumping.The small size of the tips enables rapid control of the requiredtemperature for a chemical reaction. Heat transfer and/or maintenancecan be carried out by simply placing the tip in a temperature controlledblock or chamber.

In some embodiments, the tip is able to contain about 1 to 40microliters of fluid. In a further embodiment, the tip is able tocontain about 5 to 25 microliters of fluid. In an embodiment, the tipcontains 20 microliters of fluid. In some instances, a tip can contain 1microliter of fluid or less. In other instances, a tip can contain up to100 microliters.

Where desired, the end of the tip can be blotted onto an absorbentmaterial (for example incorporated into a disposable cartridge) prior tointroduction of the next assay component to avoid contamination with asmall amount of sample and/or reagent. Due to physical forces, anyliquid drawn into a subject tip can be held at any desired location withminimal risk of the liquid draining out, even when held in a verticalorientation.

The assay unit (for example, an assay tip) can be coated with assaycapture reagents prior to use, using similar fluidics as in the assay(for example, controlled capillary or mechanical aspiration).

A capture surface (also referred to herein as a reaction site) can beformed by a binding antibody or other capture reagents bound covalentlyor by adsorption to the assay unit. The surface can then dried andmaintained in dry condition until used in an assay. In an embodiment,there is a reaction site for each analyte to be measured.

In an embodiment, the assay unit can be moved into fluid communicationwith the reagent unit and/or a sample collection unit, such that areagent or sample can interact with a reaction site where bound probescan detect an analyte of interest in the bodily fluid sample. A reactionsite can then provide a signal indicative of the presence orconcentration of the analyte of interest, which can then be detected bya detection device described herein.

In some embodiments, the location and configuration of a reaction siteis an important element in an assay device. Most, if not all, disposableimmunoassay devices have been configured with their capture surface asan integral part of the device.

In one embodiment, a molded plastic assay unit is either commerciallyavailable or can be made by injection molding with precise shapes andsizes. For example, the characteristic dimension can be a diameter of0.05-3 mm or can be a length of 3 to 30 mm. The units can be coated withcapture reagents using method similar to those used to coat microtiterplates but with the advantage that they can be processed in bulk byplacing them in a large vessel, adding coating reagents and processingusing sieves, holders, and the like to recover the pieces and wash themas needed.

The assay unit can offer a rigid support on which a reactant can beimmobilized. The assay unit is also chosen to provide appropriatecharacteristics with respect to interactions with light. For example,the assay unit can be made of a material, such as functionalized glass,Si, Ge, GaAs, GaP, SiO₂, SiN₄, modified silicon, or any one of a widevariety of gels or polymers such as (poly)tetrafluoroethylene,(poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene,PMMA, ABS, or combinations thereof. In an embodiment, an assay unitcomprises polystyrene. Other appropriate materials may be used inaccordance with the present invention. A transparent reaction site maybe advantageous. In addition, in the case where there is an opticallytransmissive window permitting light to reach an optical detector, thesurface may be advantageously opaque and/or preferentially lightscattering.

A reactant immobilized at the capture surface can be anything useful fordetecting an analyte of interest in a sample of bodily fluid. Forinstance, such reactants include, without limitation, nucleic acidprobes, antibodies, cell membrane receptors, monoclonal antibodies andantisera reactive with a specific analyte. Various commerciallyavailable reactants such as a host of polyclonal and monoclonalantibodies specifically developed for specific analytes can be used.

One skilled in the art will appreciate that there are many ways ofimmobilizing various reactants onto a support where reaction can takeplace. The immobilization may be covalent or noncovalent, via a linkermoiety, or tethering them to an immobilized moiety. Non-limitingexemplary binding moieties for attaching either nucleic acids orproteinaceous molecules such as antibodies to a solid support includestreptavidin or avidin biotin linkages, carbamate linkages, esterlinkages, amide, thiolester, (N)-functionalized thiourea, functionalizedmaleimide, amino, disulfide, amide, hydrazone linkages, and amongothers. In addition, a silyl moiety can be attached to a nucleic aciddirectly to a substrate such as glass using methods known in the art.Surface immobilization can also be achieved via a Poly-L Lysine tether,which provides a charge-charge coupling to the surface.

The assay units can be dried following the last step of incorporating acapture surface. For example, drying can be performed by passiveexposure to a dry atmosphere or via the use of a vacuum manifold and/orapplication of clean dry air through a manifold.

In many embodiments, an assay unit is designed to enable the unit to bemanufactured in a high volume, rapid manufacturing processes. Forexample, tips can be mounted in large-scale arrays for batch coating ofthe capture surface into or onto the tip. In another example, tips canbe placed into a moving belt or rotating table for serial processing. Inyet another example, a large array of tips can be connected to vacuumand/or pressure manifolds for simple processing.

In an embodiment, an assay unit can be operably coupled with a fluidtransfer device. The fluid transfer device can be operated underautomatic control without human interaction. In assay units comprisingtips, the control of the installed height of a disposable liquid tiprelies on the tapered interference attachment of the tip to the liquiddispenser. A fluid transfer device can engage the tip. In someinstances, the immersion length of a tip in liquid to be transferredmust be known to minimize the liquid contact with the outside of the tipwhich may be uncontrolled. In order to couple or adhere a tip to thefluid transfer device a hard stop can be molded at the bottom of thetapered connector which engages the nozzle of the dispenser. An airtight seal can be made by an o-ring that is half way up the taper or inthe flat bottom of the nozzle. By separating the seal function of thetip from the controlled height of the tip both can be separatelyadjusted. The modular device and fluid transfer device can enable manyassays to be performed in parallel.

The reagent units of a device can store reagents that are required toperform a give chemical reaction for detecting a given analyte ofinterest. Liquid reagents can be dispensed into small capsules that canbe manufactured from a variety of materials including, withoutlimitation, plastic such as polystyrene, polyethylene, or polypropylene.In some embodiments, the reagent units are cylindrical cups. Twoexamples of a reagent unit 401, 402 comprising a cup are shown in FIGS.8A and 8B. Where desired, the units 401, 402 fit snugly into cavities ina housing of a device. The units 401, 402 can be sealed on the opensurface to avoid spilling the reagents 411, 412 onboard. In someembodiments, the seal is an aluminized plastic and can be sealed to thecup by thermal bonding. A unit can be of any shape as is necessary tocontain a reagent. For example, a cylindrical shaped reagent unit 401 isshown in FIG. 8A, and the reagent unit contains a liquid reagent 411. Adifferent shaped reagent unit 402 is illustrated in FIG. 8B also containa liquid reagent 412. Both exemplary reagent units 401, 402 compriseoptional slight modifications near the top surface that allow the units401, 402 to fit snugly into a housing of a device as described herein.

In many embodiments of the invention the reagent units are modular. Thereagent unit can be designed to enable the unit to be manufactured in ahigh volume, rapid manufacturing processes. For example, many reagentunits can be filled and sealed in a large-scale process simultaneously.The reagent units can be filled according to the type of assay or assaysto be run by the device. For example, if one user desires differentassays than another user, the reagent units can be manufacturedaccordingly to the preference of each user, without the need tomanufacture an entire device. In another example, reagent units can beplaced into a moving belt or rotating table for serial processing.

In another embodiment, the reagent units are accommodated directly intocavities in the housing of a device. In this embodiment, a seal can bemade onto areas of housing surrounding the units.

Reagents according to the present invention include without limitationwash buffers, enzyme substrates, dilution buffers, conjugates,enzyme-labeled conjugates, DNA amplifiers, sample diluents, washsolutions, sample pre-treatment reagents including additives such asdetergents, polymers, chelating agents, albumin-binding reagents, enzymeinhibitors, enzymes, anticoagulants, red-cell agglutinating agents,antibodies, or other materials necessary to run an assay on a device. Anenzyme-labeled conjugate can be either a polyclonal antibody ormonoclonal antibody labeled with an enzyme that can yield a detectablesignal upon reaction with an appropriate substrate. Non-limitingexamples of such enzymes are alkaline phosphatase and horseradishperoxidase. In some embodiments, the reagents comprise immunoassayreagents. In general, reagents, especially those that are relativelyunstable when mixed with liquid, are confined separately in a definedregion (for example, a reagent unit) within the device.

In some embodiments, a reagent unit contains approximately about 5microliters to about 1 milliliter of liquid. In some embodiments, theunit may contain about 20-200 microliters of liquid. In a furtherembodiment, the reagent unit contains 100 microliters of fluid. In anembodiment, a reagent unit contains about 40 microliters of fluid. Thevolume of liquid in a reagent unit may vary depending on the type ofassay being run or the sample of bodily fluid provided. In anembodiment, the volumes of the reagents do not have to predetermined,but must be more than a known minimum. In some embodiments, the reagentsare initially stored dry and dissolved upon initiation of the assaybeing run on the device.

In an embodiment, the reagent units can be filled using a siphon, afunnel, a pipette, a syringe, a needle, or a combination thereof. Thereagent units may be filled with liquid using a fill channel and avacuum draw channel. The reagent units can be filled individually or aspart of a bulk manufacturing process.

In an embodiment, an individual reagent unit comprises a differentreagent as a means of isolating reagents from each other. The reagentunits may also be used to contain a wash solution or a substrate. Inaddition, the reagent units may be used to contain a luminogenicsubstrate. In another embodiment, a plurality of reagents are containedwithin a reagent unit.

In some instances, the setup of the device enables the capability ofpre-calibration of assay units and the reagent units prior to assemblyof disposables of the subject device.

In an aspect, an FS system of the invention comprises a devicecomprising assay units and reagent units comprising reagents (bothliquid and solid phase reagents). In some embodiments, at least one ofthe whole device, an assay unit, a reagent unit, or a combinationthereof is disposable. In a system of the invention, the detection of ananalyte with a device is operated by an instrument. In most embodiments,the instrument, device, and method offer an automated detection system.The automated detection system can be automated based upon a definedprotocol or a protocol provided to the system by a user.

In an aspect, a system for automated detection an analyte in a bodilyfluid sample comprises a device or cartridge, and a detection assemblyor detector for detecting the detectable signal indicative of thepresence or absence of the analyte.

In an embodiment, the user applies a sample (for example, a measured oran unmeasured blood sample) to the device and inserts the device intothe instrument. All subsequent steps are automatic, programmed either bythe instrument (hard wired), the user, a remote user or system, ormodification of the instrument operation according to an identifier (forexample, a bar code or RFID on the device).

Examples of different functions of that can be carried out using asystem of the invention include, but are not limited to, dilution of asample, removal of parts of a sample (for example, red blood cells(RBCs)), reacting a sample in an assay unit, adding liquid reagents tothe sample and assay unit, washing the reagents from the sample andassay unit, and containing liquids during and following use of thedevice. Reagents can be onboard the device in a reagent unit or in areagent unit to assembled onto the device.

An automated system can detect a particular analyte in a biologicalsample (for example, blood) by an enzyme-linked immunosorbent assay(ELISA). The system is amenable to multiplexing and is particularlysuited for detecting an analyte of interest present in a small volume ofa whole blood sample (for example, 20 microliters or less). The systemcan also detect analytes in different dilutions of a single sample,allowing different sensitivities to be tested on the same device, whendesired. All reagents, supplies, and wastes can be contained on thedevice of the system.

In use, a sample from a subject is applied to the assembled device andthe device is inserted into an instrument. In an embodiment, aninstrument can begin processing the sample by some combination ofremoval of red cells (blood sample), dilution of the sample, andmovement the sample to the assay unit. In an embodiment with multiplexedassays, a plurality of assay units is used and a portion of the sampleis moved to individual assay units in sequence or in parallel. Assayscan then be performed by a controlled sequence of incubations andapplications of reagents to the capture surfaces.

An exemplary fluid transfer device is comprised of any component capableof performing precise and accurate fluid movements. Example ofcomponents include, but are not limited to, pumps to aspirate and ejectaccurately known fluid volumes from wells or units of the device, atleast one translational stage for improving the precision and accuracyof the movement within the system. The system also comprises a detectorto detect a signal generated by a signal generator (such as an enzyme incontact with its substrate) in an assay unit. Detectors include PMTs,Diodes, CCD and the like. In the case of absorbance or fluorescencebased assays, a light source is used. For luminescence-based assays, nolight source is needed in the system instrument and a PMT or anAvalanche photodiode detector can be employed. Where desired, theinstrument has temperature regulation to provide a regulated temperatureenvironment for incubation of assays. In an embodiment of the invention,the instrument controls the temperature of the device. In a furtherembodiment, the temperature is in the range of about 30-40 degreesCelsius. In some embodiments, the temperature control by the system cancomprise active cooling. In some instances, the range of temperature isabout 0-100 degrees Celsius. For example, for nucleic acid assays,temperatures up to 100 degrees Celsius can be achieved. In anembodiment, the temperature range is about 15-50 degrees Celsius. Atemperature control unit of the system can comprise a thermoelectricdevice, such as a Peltier device.

Cartridges, devices, and systems as described herein can offer manyfeatures that are not available in existing POC systems or integratedanalysis systems. For example, many POC cartridges rely on a closedfluidic system or loop to handle small volumes of liquid in an efficientmanner. The cartridges and fluidic devices described herein can haveopen fluid movement between units of the cartridge. For example, areagent can be stored in a unit, a sample in a sample collection unit, adiluent in a diluent unit, and the capture surface can be in an assayunit, wherein in one state of cartridge, none of the units are in fluidcommunication with any of the other units. Using a fluid transfer deviceor system as described herein, the assay units do not have to be influid communication with each other. This can be advantageous in somesettings because each assay chemistry does not interact physically orchemically with others to avoid interference due to assay cross talk.The units can be movable relative to each other in order to bring someunits into fluid communication. For example, a fluid transfer device cancomprise a head that engages an assay unit and moves the assay unit intofluidic communication with a reagent unit.

The devices and systems herein can provide an effective means for highthroughput and real-time detection of analytes present in a bodily fluidfrom a subject. The detection methods may be used in a wide variety ofcircumstances including identification and quantification of analytesthat are associated with specific biological processes, physiologicalconditions, disorders or stages of disorders. As such, the systems havea broad spectrum of utility in, for example, drug screening, diseasediagnosis, phylogenetic classification, parental and forensicidentification, disease onset and recurrence, individual response totreatment versus population bases, and monitoring of therapy. Thesubject devices and systems are also particularly useful for advancingpreclinical and clinical stage of development of therapeutics, improvingpatient compliance, monitoring ADRs associated with a prescribed drug,developing individualized medicine, outsourcing blood testing from thecentral laboratory to the home or on a prescription basis, andmonitoring therapeutic agents following regulatory approval or duringclinical trials. The devices and systems can provide a flexible systemfor personalized medicine. Using the same system, a device can bechanged or interchanged along with a protocol or instructions to aprogrammable processor of the systems to perform a wide variety ofassays as described. The systems and devices herein offer many featuresof a laboratory setting in a desk-top or smaller size automatedinstrument. Because of these features, the devices are particularly wellsuited for deployment as FS devices for the HS systems of the invention.

In some embodiments, an individual be monitored by the HS is providedwith a plurality of devices to be used for detecting a variety ofanalytes. An individual may, for example, use different fluidic deviceson different days of the week. In some embodiments the software on theexternal device associating the identifier with a protocol may include aprocess to compare the current day with the day the fluidic device is tobe used based on a clinical trial for example. In another embodiment,the individual is provided different reagent units and assay units thatcan be fit into a housing of a device interchangeably. In yet anotherembodiment, as described the individual does not need a new device foreach day of testing, but rather, the system can be programmed orreprogrammed by downloading new instructions from, e.g. an externaldevice such as a server. If for example, the two days of the week arenot identical, the external device can wirelessly send notification tothe individual using any of the methods described herein or known in theart to notify them of the proper device and/or proper instructions forthe system. This example is only illustrative and can easily be extendedto, for example, notifying a subject that a fluidic device is not beingused at the correct time of day. Using these methods, the FS devices canbe rapidly adjusted as the disease being monitored. For example, the OSmay direct the FS to immediately assay individuals in contact with anindex case.

In one embodiment, a cartridge as illustrated in FIG. 5 comprises avariety of assay units and reagent units. The assay units can comprise acapture surface according to an analyte to be detected. The assay unitscan then be assembled with the rest of the device in a just-in-timefashion. In many prior art POC devices, the capture surface is integralto the device and if the capture surface is incorrect or not properlyformed, the whole device may function improperly. Using a device asdescribed herein, the capture surface and/or assay unit can beindividually quality controlled and customized independently of thereagent units and the housing of the device.

Reagent units can be filled with a variety of reagents in a similarjust-in-time fashion. This provides flexibility of the device beingcustomizable. In addition, the reagent units can be filled withdifferent volumes of reagents without affecting the stability of adevice or the chemical reactions to be run within the device. Coupledwith a system as described with a fluid transfer device, the devices andunits described herein offer flexibility in the methods and protocols ofthe assays to be run. For example, a batch of similar devices containingthe same reagents can be given to a community being monitored by the HS.After a period of monitoring, the OS identifies that the assay could beoptimized by changing the dilution of the sample and the amount ofreagent provided to the assay unit. As provided herein, the assay can bechanged or optimized by only changing the instructions to a programmableprocessor of the fluid transfer device. For example, the batch ofcartridges in the patient pool had excess diluent loaded on thecartridge. The new protocol demands four times as much diluent as theprevious protocol. Due to the methods and systems provided herein, theprotocol can be changed at the central OS server and sent to all thesystems for executing the methods with the devices without having toprovide new devices to the patient pool. In other words, a POC deviceand system as described herein can offer much of the flexibility of astandard laboratory practice where excess reagents and often excesssample are often available. Such flexibility can be acheived withoutcompromising the advantages of the POC testing scenario or thecapability to assay small sample volumes.

In some instances, wherein the units of the cartridge are separate,devices and systems provide flexibility in construction of the systemsdescribed herein. For example, a cartridge can be configured to run 8assays using an array of assay units and an array of reagent units. Dueto the features of the cartridge as described herein, the same housing,or a housing of the same design can be used to manufacture a cartridgewith up to 8 different assays than the previous cartridge. Thisflexibility is difficult to achieve in many other POC device designsbecause of the closed systems and fluid channels, and therefore thedevices may not be modular or as easy to assemble as described.

Currently, a need exists for detecting more than one analyte where theanalytes are present in widely varying concentration range, for example,one analyte is in the pg/ml concentration range and another is in theug/ml concentration range. In a non-limiting example, a viral antigenmay be detected in pg/ml range whereas a host antibody to that antigenis detected in the ug/ml range. See Table 4. The system as describedherein has the ability to simultaneously assay analytes that are presentin the same sample in a wide concentration range. Another advantage forbeing able to detect concentrations of different analytes present in awide concentration range is the ability to relate the ratios of theconcentration of these analytes to safety and efficacy of multiple drugsadministered to a patient. For example, unexpected drug-druginteractions can be a common cause of adverse drug reactions. Areal-time, concurrent measurement technique for measuring differentanalytes would help avoid the potentially disastrous consequence ofadverse drug-drug interactions. This can be useful when rapidlydeploying drugs to control an outbreak.

Being able to monitor the rate of change of an analyte concentrationand/or or concentration of pharmacodynamic (PD) or pharmacokinetic (PK)markers over a period of time in a single subject, or performing trendanalysis on the concentration, or markers of PD, or PK, whether they areconcentrations of drugs or their metabolites, can help preventpotentially dangerous situations. For example, if the HS is being usedto monitor diabetes and glucose were the analyte of interest, theconcentration of glucose in a sample at a given time as well as the rateof change of the glucose concentration over a given period of time couldbe highly useful in predicting and avoiding, for example, hypoglycemicevents. Such trend analysis has widespread beneficial implications indrug dosing regimen. When multiple drugs and their metabolites areconcerned, the ability to spot a trend and take proactive measures isoften desirable.

Accordingly, the data generated with the use of the subject fluidicdevices and systems can be utilized for performing a trend analysis onthe concentration of an analyte in a subject.

Often, multiple assays on the same cartridge may require differentdilutions or pre-treatments. The range of dilution can be substantialbetween assays. Many current POC devices offer a limited range ofdilution and therefore a limited number of assays that can bepotentially carried out on the POC device. However, a system and/orcartridge as described herein can offer a large range of dilutions,e.g., 1:2-1:10,000 due to the ability of the system to serially dilute asample. Therefore, a large number of potential assays can be performedon a single cartridge or a plurality of cartridges without modifying thedetector or reading instrument for the assays.

In an example, a system as provided herein is configured to run multiple(e.g., five or more) different target analyte detection assays. In orderto bring the expected analyte concentration within the range ofdetection of an immunoassay as described herein and commonly used in thePOC field, a sample must be diluted e.g., 3:1, 8:1, 10:1, 100:1, and2200:1, to run each of the five assays. Because the fluid transferdevice is able to hold and move fluid within the device, serialdilutions can be performed with a system as described herein to achievethese five different dilutions and detect all five different targetanalytes. As described above, the protocol for performing the assays isalso capable of being adjusted without modifying the device or thesystem.

In a laboratory setting with traditional pipetting, typically largervolumes of sample are used than in a POC setting. For example, alaboratory may analyze a blood sample withdrawn from the arm of apatient in a volume in the milliliter range. In a POC setting, manydevices and users demand that the process is fast, easy and/or minimallyinvasive, therefore, small samples (on the order of a volume in themicroliter range) such as one obtained by a fingerstick) are typicallyanalyzed by a POC device. Because of the difference in sample, currentPOC devices can lose flexibility in running an assay that is afforded ina laboratory setting. For example, to run multiple assays from a sample,a certain minimum volume can be required for each assay to allow foraccurate detection of an analyte, therefore putting some limits on adevice in a POC setting.

In another example, a system and/or fluid transfer device as describedherein provides a great deal of flexibility. For example, the fluidtransfer device can be automated to move an assay unit, an assay tip, oran empty pipette from one unit of the device to a separate unit of thedevice, not in fluid communication with each other. In some instances,this can avoid cross-contamination of the units of a device asdescribed. In other instances, it allows for the flexibility of movingseveral fluids within a device as described into contact with each otheraccording to a protocol or instructions. For example, a cartridgecomprising 8 different reagent sets in 8 different reagent units can beaddressed and engaged by a fluid transfer device in any order orcombination as is instructed by a protocol. Therefore, many differentsequences can be run for any chemical reaction to run on the device.Without changing the volume of the reagents in the cartridge or the typeof reagents in the cartridge, the assay protocol can be different ormodified without the need for a second cartridge or a second system.

For example, an FS worker orders a cartridge with a specific type ofcapture surface and specific reagents to run an assay to detect ananalyte (for example, C-reactive protein (CRP)) in a sample. Theprotocol the FS worker originally planned for may require 2 washingsteps and 3 dilution steps. After the FS worker has received the deviceand system, those at the OS site responsible for the deployed FS devicesdetermines that the protocol should have 5 washing steps and only 1dilution step. The devices and systems herein can allow the flexibilityfor this change in protocol without having to reconfigure the device orthe system. In this example, only a new protocol or set of instructionsare needed to be sent from the OS component to the programmableprocessor of the FS system or the fluid transfer device.

In another example, a system as provided herein is configured to runfive different target analyte detection assays, wherein each assay needsto be incubated at a different temperature. In many prior art POCdevices, incubation of multiple assays at different temperatures is adifficult task because the multiple assays are not modular and thecapture surfaces cannot be moved relative to the heating device. In asystem as described herein, wherein an individual assay unit isconfigured to run a chemical reaction, an individual assay unit can beplace in an individual heating unit. In some embodiments, a systemcomprises a plurality of heating units. In some instances, a systemcomprises at least as many heating units as assay units. Therefore, aplurality of assays can be run as a plurality of temperatures.

Systems and devices as described herein can also provide a variety ofquality control measures not previously available with many prior artPOC devices. For example, because of the modularity of a device, theassay units and reagents units can be quality controlled separately fromeach other and/or separately from the housing and/or separately from asystem or fluid transfer device. Exemplary methods and systems ofquality control offered by the systems and devices herein are described.

An FS system as described for use with the invention can run a varietyof assays, regardless of the analyte being detected from a bodily fluidsample. A protocol dependent on the identity of the device may betransferred from the external OS component where it can be stored to areader assembly to enable the reader assembly to carry out the specificprotocol on the device. In some embodiments, the device has anidentifier (ID) that is detected or read by an identifier detectordescribed herein. The identifier detector can communicate with acommunication assembly via a controller which transmits the identifierto an external device. Where desired, the external device sends aprotocol stored on the external device to the communication assemblybased on the identifier. The protocol to be run on the system maycomprise instructions to the controller of the system to perform theprotocol, including but not limited to a particular assay to be run anda detection method to be performed. Once the assay is performed by thesystem, a signal indicative of an analyte in the bodily fluid sample isgenerated and detected by a detection assembly of the system. Thedetected signal may then be communicated to the communications assembly,where it can be transmitted to the external device for processing,including without limitation, calculation of the analyte concentrationin the sample.

In some embodiments, the identifier may be a bar code identifier with aseries of black and white or reflective lines or blocks, which can beread by an identifier detector such as a bar code reader, which are wellknown or an Radio-frequency identification (RFID) tag with anappropriate detector. Other identifiers could be a series ofalphanumerical values, colors, raised bumps, or any other identifierwhich can be located on a device and be detected or read by anidentifier detector. The identifier detector may also be an LED thatemits light which can interact with an identifier which reflects lightand is measured by the identifier detector to determine the identity ofa device. In some embodiments the identifier may comprise a storage ormemory device and can transmit information to an identificationdetector. In some embodiments a combination of techniques may be used.In some embodiments, the detector is calibrated by use of an opticalsource, such as an LED.

In an example, a bodily fluid sample can be provided to a device, andthe device can be inserted into a system. In some embodiments the deviceis partially inserted manually, and then a mechanical switch in thereader assembly automatically properly positions the device inside thesystem. Any other mechanism known in the art for inserting a disk orcartridge into a system may be used. In some embodiments, manualinsertion may be required.

In some embodiments a method of automatically selecting a protocol to berun on a system comprises providing a device comprising an identifierdetector and an identifier; detecting the identifier; transferring saididentifier to the external OS component of the systems of the invention;and selecting a protocol to be run on the system from a plurality ofprotocols on external OS component associated with said identifier.

In one embodiment, an FS system of the invention for automated detectionof a plurality of analytes in a bodily fluid sample comprises: a fluidicdevice (such as those described herein) comprising: a sample collectionunit configured to contain the bodily fluid sample; an array of assayunits, wherein an individual assay unit of said array of assay units isconfigured to run a chemical reaction that yields a signal indicative ofan individual analyte of said plurality of analytes being detected; andan array of reagent units, wherein an individual reagent unit of saidarray of reagent units contains a reagent. The system further comprisesa fluid transfer device comprising a plurality of heads, wherein anindividual head of the plurality of heads is configured to engage theindividual assay unit, and wherein said fluid transfer device comprisesa programmable processor configured to direct fluid transfer of thebodily fluid sample from the sample collection unit and the reagent fromthe individual reagent unit into the individual assay unit. For example,an individual assay unit comprises a reagent and is configured is to runa chemical reaction with that reagent.

In some instances, the configuration of the processor to direct fluidtransfer effects a degree of dilution of the bodily fluid sample in thearray of assay units to bring signals indicative of the plurality ofanalytes being detected within a detectable range, such that saidplurality of analytes are detectable with said system. In an example,the bodily fluid sample comprises at least two analytes that are presentat concentrations that differ by at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,15, 50, or 100 orders of magnitude. In an example the bodily fluidsample is a single drop of blood. In an embodiment, the concentrationsof at least two analytes present in a sample differs by up to 10 ordersof magnitude (for example, a first analyte is present at 0.1 pg/mL and asecond analyte is present at 500 ug/mL. In another example, some proteinanalytes are found at concentrations of greater than 100 mg/mL, whichcan extend the range of interest to about twelve orders of magnitude.

A degree of dilution of the bodily fluid sample can bring the signalsindicative of the at least two analytes within the detectable range. Inmany instances, a system further comprises a detector, such as aphotomultiplier (PMT). With a photomultiplier, for example, a detectablerange of the detector can be about 10 to about 10 million counts persecond. Each count corresponds to a single photon. In some instances,PMTs are not 100% efficient and the observed count rate may be slightlylower than, but still close to, the actual number of photons reachingthe detector per unit time. In some instances, counts are measured inabout ten intervals of about one second and the results are averaged. Insome embodiments, ranges for assays are 1000-1,000,000 counts per secondwhen using a PMT as a detector. In some instances, count rates as low as100 per second and count rates as high as 10,000,000 are measurable. Thelinear response range of PMTs (for example, the range where count rateis directly proportional to number of photons per unit time) can beabout 1000-3,000,000 counts per second. In an example, an assay has adetectable signal on the low end of about 200-1000 counts per second andon the high end of about 10,000-2,000,000 counts per second. In someinstances for protein biomarkers, the count rate is directlyproportional to alkaline phosphatase bound to the capture surface andalso directly proportional to the analyte concentration. Other exemplarydetectors include avalanche photodiodes, avalanche photodiode arrays,CCD arrays, super-cooled CCD arrays. Many other detectors have an outputthat is digital and generally proportional to photons reaching thedetector. The detectable range for exemplary detectors can be suitableto the detector being used.

An individual head of a fluid transfer device can be configured toadhere to the individual assay unit. The fluid transfer device can be apipette, such as an air-displacement pipette. The fluid transfer devicecan be automated. For example, a fluid transfer device can furthercomprise a motor in communication with a programmable processor and themotor can move the plurality of heads based on a protocol from theprogrammable processor. As described, an individual assay unit can be apipette tip, for example, a pipette tip with a capture surface orreaction site.

Often times, in a POC device, such as the systems and devices describedherein, the dilution factor must be estimated and reasonably precise.For example, in environments where non-expert users operate the systemthere needs to be ways of ensuring accurate dilution of a sample.

As described herein, a fluid transfer device can affect a degree ofdilution of a sample to provide accurate assay results. For example, aprogrammable fluid transfer device can be multi-headed to dilute orserially dilute samples as well as provide mixing of a sample anddiluent. A fluid transfer device can also provide fluid movement in POCdevices.

As described, the systems and devices herein can enable many features ofthe flexibility of laboratory setting in a POC environment. For example,samples can be collected and manipulated automatically in a table topsize or smaller device or system. A common issue in POC devices isachieving different dilution ranges when conducting a plurality ofassays, wherein the assays may have significantly different sensitivityor specificity. For example, there may be two analytes in a sample, butone analyte has a high concentration in the sample and the other analytehas a very low concentration. As provided, the systems and devicesherein can dilute the sample to significantly different levels in orderto detect both analytes. For example, if the analyte is in a highconcentration, a sample can be serially diluted to the appropriatedetection range and provided to a capture surface for detection. In thesame system or device, a sample with an analyte in a low concentrationmay not need to be diluted. In this manner, the assay range of the POCdevices and systems provided herein can be expanded from many of thecurrent POC devices.

A fluid transfer device can be part of a system that is a bench-topinstrument. The fluid transfer device can comprise a plurality of heads.Any number of heads as is necessary to detect a plurality of analytes ina sample is envisioned for a fluid transfer device of the invention. Inan example, a fluid transfer device has about eight heads mounted in aline and separated by a distance. In an embodiment, the heads have atapered nozzle that engages by press fitting with a variety of tips,such as assay unit or sample collection units as described herein. Thetips can have a feature that enables them to be removed automatically bythe instrument and disposed into in a housing of a device as describedafter use. In an embodiment, the assay tips are clear and transparentand can be similar to a cuvette within which an assay is run that can bedetected by an optical detector such as a photomultiplier tube.

In an example, the programmable processor of an FS system can compriseinstructions or commands and can operate a fluid transfer deviceaccording to the instructions to transfer liquid samples by eitherwithdrawing (for drawing liquid in) or extending (for expelling liquid)a piston into a closed air space. Both the volume of air moved and thespeed of movement can be precisely controlled, for example, by theprogrammable processor.

Mixing of samples (or reagents) with diluents (or other reagents) can beachieved by aspirating components to be mixed into a common tube andthen repeatedly aspirating a significant fraction of the combined liquidvolume up and down into a tip. Dissolution of reagents dried into a tubecan be performed in a similar fashion. Incubation of liquid samples andreagents with a capture surface on which is bound a capture reagent (forexample an antibody) can be achieved by drawing the appropriate liquidinto the tip and holding it there for a predetermined time. Removal ofsamples and reagents can be achieved by expelling the liquid into areservoir or an absorbent pad in a device as described. Another reagentcan then be drawn into the tip according to instructions or protocolfrom the programmable processor.

In an example as illustrated in FIG. 9, a liquid 1111 previously in atip 1101 can leave a thin film 1113 within the tip 1101 when expelled.Therefore, a system can use the action of the leading (for exampleuppermost) portion of the next liquid 1112 to scour the previouslypresent liquid 1111 from the tip 1101. The portion of the subsequentliquid contaminated with the liquid previously present 1113 can be heldwithin the top of the tip 1101 where it does not continue to interactwith the capture surface 1102. The capture surface 1102 can be in adefined area of the tip 1101 such that the previous liquid 1111 does notreact with the capture surface 1102, for example as shown in FIG. 9, thecapture surface 1102 occupies a defined portion of the cylindrical partof the tip 1101 not extending all the way up to the boss of the tip. Inmany instances, incubation time is short (for example 10 minutes) andseparation of the contaminated zone of liquid is relatively large (>1mm) so diffusion or the active components of the contaminated portion ofliquid 1113 does not occur rapidly enough react with the capture surface1102 during the incubation. For many high sensitivity assays, there is arequirement to remove one reagent or wash the capture surface (forexample, a detector antibody which is labeled with the assay signalgenerator). In an example, a fluid transfer device of a system describedherein can provide washing by adding further removal and aspirationcycles of fluid transfer, for example, using a wash reagent. In anexample, four wash steps demonstrated that the unbound detector antibodyin contact with the capture surface is reduced by a factor of betterthan 10⁶-fold. Any detector antibody non-specifically bound to thecapture surface (highly undesirable) can also be removed during thiswash process.

Extension of the range of an assay can be accomplished by dilution ofthe sample. In POC assay systems using disposable cartridges containingthe diluent there is often a practical limit to the extent of dilution.For example, if a small blood sample is obtained by fingerstick (forexample, about 20 microliters) is to be diluted and the maximum volumeof diluent that can be placed in a tube is 250 microliters, thepractical limit of dilution of the whole sample is about 10-fold. In anexample herein, a system can aspirate a smaller volume of the sample(for example about 2 microliters) making the maximum dilution factorabout 100-fold. For many assays, such dilution factors are acceptablebut for an assay like that of CRP (as described in the examples herein)there is a need to dilute the sample much more. Separation-based ELISAassays can have an intrinsic limitation in the capacity of the capturesurface to bind the analyte (for example equivalent to about a fewhundred ng/ml in the diluted sample for a typical protein analyte). Someanalytes are present in blood at hundreds of micrograms/ml. Even whendiluted by 100-fold, the analyte concentration may be outside the rangeof calibration. In an exemplary embodiment of a system, device, andfluid transfer device herein, multiple dilutions can be achieved byperforming multiple fluid transfers of the diluent into an individualassay unit or sample collection unit. For example, if the concentrationof an analyte is very high in a sample as described above, the samplecan be diluted multiple times until the concentration of the analyte iswithin an acceptable detection range. The systems and methods herein canprovide accurate measurements or estimations of the dilutions in orderto calculate the original concentration of the analyte.

In an embodiment, an FS system as described herein can move a liquidsample and move an assay unit. A system can comprise a heating block anda detector. In order to move a liquid sample, a system may provideaspiration-, syringe-, or pipette-type action. In an exemplaryembodiment, a fluid transfer device for moving a liquid sample is apipette and pipette head system. The number of pipette devices requiredby the system can be adjusted according to the type of analyte to bedetected and the number of assays being run. The actions performed bythe pipette system can be automated or operated manually by a user.

FIG. 10 demonstrates an example of a fluid transfer device 520 andsystem 500 as described herein. The fluid transfer device system canmove eight different or identical volumes of liquid simultaneously usingthe eight different heads 522. For example, the cartridge (or device asdescribed herein) 510 comprises eight assay units 501. Individual assayunits 501 are configured according to the type of assay to be run withinthe unit 501. Individual assay units 501 may require a certain volume ofsample. An individual head 522 can be used to distribute a proper amountof sample to an individual assay unit 501. In this example, each head522 corresponds to an addressed individual assay unit 501.

The fluid transfer device mechanism 520 can also be used to distributereagents from the reagent units. Different types of reagents include aconjugate solution, a wash solution, and a substrate solution. In anautomated system, the stage 530 on which the device 510 sits can bemoved to move the device 510 relative to the positioning of the assayunits 501 and heads 522 and according to the steps necessary to completean assay as demonstrated in FIG. 10. Alternatively, the heads 522 andtips 501 or the fluid transfer device 520 can be moved relative to theposition of the device 510.

In some embodiments, a reagent is provided in dry form and rehydratedand/or dissolved during the assay. Dry forms include lyophilizedmaterials and films coated on and adherent to surfaces.

A FS system can comprise a holder or engager for moving the assay unitsor tips. An engager may comprise a vacuum assembly or an assemblydesigned to fit snugly into a boss of an assay unit tip. For example, ameans for moving the tips can be moved in a manner similar to the fluidtransfer device heads. The device can also be moved on a stage accordingto the position of an engager or holder.

In an embodiment, an instrument for moving the tips is the same as aninstrument for moving a volume of sample, such as a fluid transferdevice as described herein. For example, a sample collection tip can befit onto a pipette head according to the boss on the collection tip. Thecollection tip can then be used to distribute the liquid throughout thedevice and system. After the liquid has been distributed, the collectiondip can be disposed, and the pipette head can be fit onto an assay unitaccording to the boss on the assay unit. The assay unit tip can then bemoved from reagent unit to reagent unit, and reagents can be distributedto the assay unit according to the aspiration- or pipette-type actionprovided by the pipette head. The pipette head can also perform mixingwithin a collection tip, assay unit, or reagent unit by aspiration- orsyringe-type action.

An FS system can comprise a heating block for heating the assay or assayunit and/or for control of the assay temperature. Heat can be used inthe incubation step of an assay reaction to promote the reaction andshorten the duration necessary for the incubation step. A system cancomprise a heating block configured to receive an assay unit. Theheating block can be configured to receive a plurality of assay unitsfrom a device as described herein. For example, if 8 assays are desiredto be run on a device, the heating block can be configured to receive 8assay units. In some embodiments, assay units can be moved into thermalcontact with a heating block using the means for moving the assay units.The heating can be performed by a heating means known in the art.

An exemplary FS system 600 as described herein is demonstrated in FIG.11. The system 600 comprises a translational stage 630 onto which adevice 610 (or cartridge in this example) is placed either manually orautomatically or a combination of both. The system 600 also comprises aheating block 640 that can be aligned with the assay units 611 of thedevice 610. As shown in FIG. 11, the device 610 comprises a series of 8assay units 611 and multiple corresponding reagent units 612, and theheating block 640 also comprises an area 641 for at least 8 units to beheated simultaneously. Each of the heating areas 641 can provide thesame or different temperatures to each individual assay unit 611according to the type of assay being run or the type of analyte beingdetected. The system 600 also comprises a detector (such as aphotomultiplier tube) 650 for detection of a signal from an assay unit611 representative of the detection of an analyte in a sample.

In an embodiment, a sensor is provided to locate an assay unit relativeto a detector when an assay is detected.

In an embodiment, the detector is a reader assembly housing a detectionassembly for detecting a signal produced by at least one assay on thedevice. The detection assembly may be above the device or at a differentorientation in relation to the device based on, for example, the type ofassay being performed and the detection mechanism being employed. Thedetection assembly can be moved into communication with the assay unitor the assay unit can be moved into communication with the detectionassembly.

In many instances, an optical detector is provided and used as thedetection device. Non-limiting examples include a photodiode,photomultiplier tube (PMT), photon counting detector, avalanche photodiode, or charge-coupled device (CCD). In some embodiments a pin diodemay be used. In some embodiments a pin diode can be coupled to anamplifier to create a detection device with sensitivity comparable to aPMT. Some assays may generate luminescence as described herein. In someembodiments chemiluminescence is detected. In some embodiments adetection assembly could include a plurality of fiber optic cablesconnected as a bundle to a CCD detector or to a PMT array. The fiberoptic bundle could be constructed of discrete fibers or of many smallfibers fused together to form a solid bundle. Such solid bundles arecommercially available and easily interfaced to CCD detectors.

A detector can also comprise a light source, such as a bulb or lightemitting diode (LED). The light source can illuminate an assay in orderto detect the results. For example, the assay can be a fluorescenceassay or an absorbance assay, as are commonly used with nucleic acidassays. The detector can also comprise optics to deliver the lightsource to the assay, such as a lens or fiber optics.

In some embodiments, the detection system may comprise non-opticaldetectors or sensors for detecting a particular parameter of a subject.Such sensors may include temperature, conductivity, potentiometricsignals, and amperometric signals, for compounds that are oxidized orreduced, for example, O₂, H₂O₂, and I₂, or oxidizable/reducible organiccompounds.

A device and system may, after manufacturing, be shipped to the enduser, together or individually. The device or system of the inventioncan be packaged with a user manual or instructions for use. In anembodiment, the system of the invention is generic to the type of assaysrun on different devices. Because components of the device can bemodular, a user may only need one system and a variety of devices orassay units or reagent units to run a multitude of assays in apoint-of-care environment. In this context, a system can be repeatedlyused with multiple devices, and it may be necessary to have sensors onboth the device and the system to detect such changes during shipping,for example. During shipping, pressure or temperature changes can impactthe performance of a number of components of the present system, and assuch a sensor located on either the device or system can relay thesechanges to, for example, the external device so that adjustments can bemade during calibration or during data processing on the externaldevice. For example, if the temperature of a fluidic device is changedto a certain level during shipping, a sensor located on the device coulddetect this change and convey this information to the system when thedevice is inserted into the system by the user. There may be anadditional detection device in the system to perform these tasks, orsuch a device may be incorporated into another system component. In someembodiments information may be transmitted to either the system or theexternal device, such as the OS component of the invention, or apersonal computer at a local installation. The transmission may comprisewired and/or wireless connections. Likewise, a sensor in the system candetect similar changes. In some embodiments, it may be desirable to havea sensor in the shipping packaging as well, either instead of in thesystem components or in addition thereto. For example, adverseconditions that would render an assay cartridge or system invalid thatcan be sensed can include exposure to a temperature higher than themaximum tolerable or breach of the cartridge integrity such as moisturepenetration.

In an embodiment, the system comprises a communication assembly capableof transmitting and receiving information wirelessly from an externaldevice, e.g., the OS component of the present invention. Such wirelesscommunication can use, without limitation, Wifi, Bluetooth, Zigbee,satellite, cellular or RTM technology. Various communication methods canbe used, such as a dial-up wired connection with a modem, a direct linksuch as a T1, ISDN, or cable line. In some embodiments, a wirelessconnection is established using exemplary wireless networks such ascellular, satellite, or pager networks, GPRS, or a local data transportsystem such as Ethernet or token ring over a local area network. In someembodiments the information is encrypted before it is transmitted. Insome embodiments the communication assembly may contain a wirelessinfrared communication component for sending and receiving information.The system may include integrated graphic cards to facilitate display ofinformation.

In some embodiments the communication assembly can have a memory orstorage device, for example localized RAM, in which the informationcollected can be stored. A storage device may be required if informationcan not be transmitted at a given time due to, for example, a temporaryinability to wirelessly connect to a network. The information can beassociated with the device identifier in the storage device. In someembodiments the communication assembly can retry sending the storedinformation after a certain amount of time.

In some embodiments an external device, e.g., the OS portal component ofthe invention, communicates with the communication assembly within thereader assembly. An external device can wirelessly or physicallycommunicate with the FS system, but can also communicate with a thirdparty, including without limitation an individual, medical personnel,clinicians, laboratory personnel, or others in the health care industry.

An exemplary method and system is demonstrated in FIG. 12. In theexample of FIG. 12, a patient delivers a blood sample to a device asdescribed herein and then the device is inserted into a reader, whereinthe reader can be desktop system capable of reading an analyte in theblood sample. The reader can be a system as described herein. The readercan be a bench-top or desk-top system and can be capable of reading aplurality of different devices as described herein. The reader or systemis capable of carrying out a chemical reaction and detecting or readingthe results of the chemical reaction. In the example in FIG. 12, areader is automated according to a protocol sent from an external device(for example, a server comprising a user interface). A reader can alsosend the results of the detection of the chemical reaction to the serverand user interface. In an exemplary system, the user (for example,medical personnel such as a physician or researcher) can view andanalyze the results as well as decide or develop the protocol used toautomate the system. Results can also be stored locally (on the reader)or on the server system. The server can also host patient records, apatient diary, and patient population databases.

FIG. 13 illustrates the process flow of building a system for assessingthe medical condition of an individual according to an embodiment of theHS system disclosed herein. The patient inputs personal data and ormeasurements from a device, reader, and/or system as described hereininto a database as may be present on a server, e.g., the OS component.The FS system can configured to display the personal data on a patientstation display. In some embodiments, the FS station display isinteractive and the individual can modify inputted data. The OS databasecontains data from other individuals being monitored by the HealthShield. The HS database can also include data from the other individualscollected historically from public or private institutions. In someembodiments, data from other individuals is internal data from aclinical study.

FIG. 13 also illustrates the flow of data from reader collection datathat includes the data from the subject to a server that is connectedover a public network. The server can manipulate the data or can justprovide the data to a user station. The patient data may also be inputto the server separately from the data pertaining to a medical conditionthat is stored in a database. FIG. 13 also demonstrates a user stationdisplay and the flow of information to medical personnel or a user. Forexample, using the exemplary process flow of FIG. 13, a patient at homecan input a bodily fluid sample into a cartridge of the invention asdescribed herein and place it in a system or reader as described herein.The patient can view the data from the system at a patient stationdisplay and/or modify or input new data into the process flow. The datafrom the patient can then travel over a public network, such as theinternet, for example, in an encrypted format, to a server comprising anetwork interface and a processor, wherein the server is located at acentral computing hub or in a clinical trial center. The server can usemedical condition data to manipulate and understand the data from theuser and then send the results over a public network as described to auser station. The user station can be in a medical office or laboratoryand have a user station display to display the results of the assay andmanipulation of the patient data to the medical personnel. In thisexample, the medical personnel can receive results and analysis of asample from a patient from a test that the patient administered in analternate location such as the patient's home. Other embodiments andexample of systems and components of systems are described herein.

The OS component of the HS system can store protocols to be run on an FSsystem. The protocol can be transmitted to the communication assembly ofa FS system after the OS has received an identifier indicating whichdevice has been inserted in the FS system. In some embodiments aprotocol can be dependent on a device identifier. In some embodimentsthe OS component stores more than one protocol for each field device. Inother embodiments patient information on the external device includesmore than one protocol. In some instances, the OS component storesmathematical algorithms to process a photon count sent from acommunication assembly and in some embodiments to calculate the analyteconcentration in a bodily fluid sample.

Having the FS and OS components of the systems integrated over a networkconnection provides a number of advantages. For example, the informationcan be transmitted from the Operating System back to not only the FSreader assembly, but to other parties or other external devices, forexample without limitation, a PDA or cell phone. Such communication canbe accomplished via a wireless network as disclosed herein. In someembodiments a calculated analyte concentration or other patientinformation can be sent to, for example but not limited to, medicalpersonnel or the patient. In a non-limiting example, a quarantine noticecan be sent to both the infected individual and to medical personnel whocan put into place the quarantine.

In some embodiments, the data generated with the use of the subjectdevices and systems can be utilized for performing a trend analysis onthe concentration of an analyte in a subject.

Another advantage as described herein is that assay results can besubstantially immediately communicated to any third party who maybenefit from obtaining the results. For example, once the analyteconcentration is determined at the Operating System component, it can betransmitted to a patient or medical personnel who may need to takefurther action. This might include identification of an index case. Thecommunication step to a third party can be performed wirelessly asdescribed herein, and by transmitting the data to a third party's handheld device, the third party can be notified of the assay resultsvirtually anytime and anywhere. Thus, in a time-sensitive scenario, apatient may be contacted immediately anywhere if urgent medical actionmay be required.

By detecting a device based on an identifier associated with a fluidicdevice after it is inserted in the FS system, the system allows forfluidic device-specific protocols to be downloaded from an externaldevice, e.g., the OS component, and run. In some embodiments the OScomponent can store a plurality of protocols associated with the systemor associated with a particular individual or group of individuals. Forexample, when the identifier is transmitted to the OS component,software on the OS component, such as a database, can use the identifierto identify protocols stored in the database associated with theidentifier. If only one protocol is associated with the identifier, forexample, the database can select the protocol and software on theexternal device can then transmit the protocol to the communicationassembly of the system. The ability to use protocols specificallyassociated with a device allows for any component of a device of theinvention to be used with a single system, and thus virtually anyanalyte of interest can be detected with a single system.

In some embodiments multiple protocols may be associated with a singleidentifier. For example, if it is beneficial to detect from the sameindividual an analyte once a week, and another analyte twice a week,protocols on the external device associated with the identifier can alsoeach be associated with a different day of the week, so that when theidentifier is detected, the software on the external device can select aspecific protocol that is associated with the day of the week. Suchoptimized testing can reduce the cost of the HS system by onlyperforming assays according to an optimized schedule.

In some embodiments, an individual is provided with a plurality ofdevices to use to detect a variety of analytes. The individual may, forexample, use different devices on different days of the week. In someembodiments the software on the Operating System associating theidentifier with a protocol may include a process to compare the currentday with the day the device is to be used based on a clinical trial forexample. If for example, the two days of the week are not identical, theOperating System can wirelessly send notification to the subject usingany of the methods described herein or known in the art to notify themthat an incorrect device is in the system and also of the correct deviceto use that day. This example is only illustrative and can easily beextended to, for example, notifying a subject that a device is not beingused at the correct time of day.

The system can also use a networking method of assessing the medicalcondition of a subject. A system of communicating information may or maynot include a reader for reading subject data. For example, if biomarkerdata is acquired by a microfluidic point-of-care device, the valuesassigned to different individual biomarkers may be read by the deviceitself or a separate device. Another example of a reader would be a barcode system to scan in subject data that has been entered in anelectronic medical record or a physician chart. A further example of areader would consist of an electronic patient record database from whichsubject data could be directly obtained via the communications network.In this way, the efficacy of particular drugs can be determined inreal-time, thereby helping to determine whether a different mitigationstrategy should be put into place.

(b) Field System Methods

The FS devices described herein provide an effective means for real-timedetection of analytes present in a bodily fluid from a subject.Accordingly, in an embodiment, the present invention makes use of amethod of detecting an analyte in a bodily fluid sample comprisingproviding a blood sample to a FS device, allowing the sample to reactwithin at least one assay unit of the device, and detecting thedetectable signal generated from the analyte in the blood sample.

FIG. 5 demonstrates an exemplary embodiment of a FS device comprising atleast one assay unit and at least one reagent unit. The assay units (forexample, designated as sample tips and calibrator tips in FIG. 5) cancontain a capture surface and the reagent units can contain items suchas conjugates, washes, and substrates. The device exemplified in FIG. 5also comprises a whole blood sample collection tip, a plasma samplecollection tip, a blood input well, a beads well or plasma separationwell, a tip touch-off or blotting pad, a dilution well, a diluted plasmasample well or plasma diluent well, collection tip disposal areas.

In an embodiment, a method comprises performing an Enzyme-linkedImmunosorbent Assay (ELISA). In an example, a sample is provided to asample collection unit of a device as described herein. The device isthen inserted into a reader system, wherein reader system detects thetype of cartridge or device that is inserted. The reader system can thencommunicate with an external device, e.g., the OS component of the HSsystem, to receive a set of instructions or protocol that allows thereader system to perform the desired assay or assays of the cartridge.The protocol can be sent to the programmable processor of a fluidtransfer device of the reader system. In an example, the fluid transferdevice engages a sample tip of the cartridge and picks up a certainvolume of the sample from the sample collection unit and moves it to apretreatment unit where red blood cells are removed. The plasma of thesample can then be aspirated into a plasma tip or any assay tip by thefluid transfer device according to the protocol. The tip containing theplasma can then pick up a diluent to dilute the sample as is necessaryfor the assays to be run. Many different dilutions can be carried byusing serial dilutions of the sample. For example, each assay tip orassay unit can contain a sample of a different dilution. After thesample is aspirated into an assay unit by the fluid transfer device, theassay unit can then be incubated with the sample to allow any targetanalyte present to attach to the capture surface. Incubations asdescribed in this example can be at the system or room temperature forany period of time, for example 10 minutes, or can be in a heatingdevice of the system as described herein. The assay unit can engage areagent unit addressed with a reagent corresponding to the assay to berun in each individual assay unit that have a capture surface for thatassay. In this example, the first reagent is a detector solution of anELISA, for example, comprising a detector antibody such as a labeledanti-protein antibody different from that of the capture surface. Thedetector solution is then aspirated out of the assay unit and then awash solution can be aspirated into the assay unit to remove any excessdetector solution. Multiple wash steps can be used. The final reagent tobe added is an enzymatic substrate which causes the bound detectorsolution to chemiluminesce. In some embodiments, the results of theassay are read by a detector of the system while the tip still containsthe assay product. In other embodiments, the enzymatic substrate isexpelled from the assay unit and the results of the assay are read by adetector of the system. At each step as described, incubations can occuras necessary as described herein. In this example, the entire processafter putting the cartridge into the system is automated and carried outby a protocol or set of instructions to the programmable system.

One exemplary method proceeds with delivering a blood sample into theblood input well. The sample can then be picked up by a collection tipand inserted into the plasma separation well. Alternatively, the bloodcan be deposited directly into a well containing a blood separator. Forexample, plasma separation can be carried out by a variety of methods asdescribed herein. In this example, plasma separation proceeds usingmagnetizable beads and antibodies to remove the components of the bloodthat are not plasma. The plasma can then be carried by a plasmacollection tip as to not contaminate the sample with the whole bloodcollection tip. In this example, the plasma collection tip can pick-up apredetermined amount of diluent and dilute the plasma sample. Thediluted plasma sample is then distributed to the assay units (sampletips) to bind to a capture surface. The assay units can be incubated toallow for a capture reaction to be carried out. The assay unit then canbe used to collect a conjugate to bind with the reaction in the assayunit. The conjugate can comprise an entity that allows for the detectionof an analyte of interest by a detector, such as an optical detector.Once conjugate has been added to the assay unit, the reaction can beincubated. In an exemplary method using an exemplary device of FIG. 5, areagent unit containing a wash for the conjugate is then accessed by theassay unit (sample tip) to remove any excess conjugate that caninterfere with any analyte detection. After washing away excessconjugate, a substrate can be added to the assay unit for detection. Inaddition, in the example of FIG. 5 and this method, a calibrator tipassay unit can be used to carry out all of the methods described in thisparagraph except the collection and distribution of the sample.Detection and measurements using the calibrator tip assay unit can beused to calibrate the detection and measurements of the analyte from thesample. Other processes and methods similar to those used in thisexample are described hereinafter.

Any bodily fluids suspected to contain an analyte of interest can beused in conjunction with the system or devices of the invention. Forexample, the input well or sample collection unit in the example of FIG.5 can collect of contain any type of commonly employed bodily fluidsthat include, but are not limited to blood, serum, saliva, urine,gastric and digestive fluid, tears, stool, semen, vaginal fluid,interstitial fluids derived from tumorous tissue liquids extracted fromtissue samples, and cerebrospinal fluid. In an embodiment, the bodilyfluid is blood and can be obtained by a fingerstick. In an embodiment,the bodily fluid sample is a blood plasma sample. In another embodiment,the bodily fluid sample is an unmodified blood sample.

A bodily fluid may be drawn from a patient and distributed to the devicein a variety of ways including, but not limited to, lancing, injection,or pipetting. In one embodiment, a lancet punctures the skin anddelivers the sample into the device using, for example, gravity,capillary action, aspiration, or vacuum force. The lancet may be onboardthe device, or part of a reader assembly, or a stand alone component.Where needed, the lancet may be activated by a variety of mechanical,electrical, electromechanical, or any other known activation mechanismor any combination of such methods. In another embodiment where noactive mechanism is required, an individual can simply provide a bodilyfluid to the device, as could occur, for example, with a saliva sample.The collected fluid can be placed into a collection well or unit of thedevice. In some embodiments, there is a user activated lancet and samplecollecting capillary within the device.

The volume of bodily fluid to be used with a method or device describedherein is generally less than about 500 microliters, further can bebetween about 1 to 100 microliters. Where desired, a sample of 1 to 50microliters, 1 to 40 microliters, 1 to 30 microliters, 1 to 10microliters or even 1 to 3 microliters can be used for detecting ananalyte using the subject fluidic device. In an embodiment, the sampleis 20 microliters. A slight excess of sample may be collected over thatrequired for the assay, e.g., 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%,12%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%,80%, 85%, 95%, or 100% extra. In some embodiments, more than 100% extrasample volume is collected. For example, when the sample volume requiredfor the assays is, for example, 15 uL, the system may use a volume inthe range 16-50 uL.

In an embodiment, the volume of bodily fluid used for detecting ananalyte in the field is one drop of fluid. For example, one drop ofblood from a pricked finger can provide the sample of bodily fluid to beanalyzed according to the invention.

In some embodiments, the bodily fluids are used directly for detectingthe analytes present in the bodily fluid without further processing.Where desired, however, the bodily fluids can be pre-treated beforeperforming the analysis with a device. The choice of pre-treatments willdepend on the type of bodily fluid used and/or the nature of the analyteunder investigation. For instance, where the analyte is present at lowlevel in a sample of bodily fluid, the sample can be concentrated viaany conventional means to enrich the analyte. Methods of concentratingan analyte include but are not limited to drying, evaporation,centrifugation, sedimentation, precipitation, and amplification. Wherethe analyte is a nucleic acid, it can be extracted using various lyticenzymes or chemical solutions or using nucleic acid binding resinsfollowing the accompanying instructions provided by manufacturers. Forblood or plasma samples, the sample may be mixed with an anticoagulantsuch as EDTA or heparin. These agents may conveniently be added fromdried form. Where the analyte is a molecule present on or within a cell,extraction can be performed using lysing agents including but notlimited to anticoagulants such as EDTA or heparin, a denaturingdetergent such as SDS or non-denaturing detergent such as Thesit®,sodium deoxycholate, triton X-100, and tween-20.

In an embodiment, a user collects a sample of bodily fluid with asyringe. The sample can enter the syringe through a capillary tube. Inan embodiment measuring an analyte in a blood sample, the subjectperforms a fingerstick and touches the outer end of the glass capillaryto the blood so that blood is drawn by capillary action and fills thecapillary with a volume. In some instances, the sample volume is known.In some embodiments, the sample volume is in the range of about 5-20microliters or other volume ranges as described herein.

In another embodiment, a method and system is provided to obtain aplasma sample substantially free of red blood cells from a blood sample.When conducting an assay, the analytes are often contained in the bloodplasma, and the red blood cells can interfere with a reaction.

Often, when measuring a blood sample, the analytes of interest are inthe serum or plasma. For clinical purposes, the final reportedconcentration of multiple blood tests often needs to relate to theconcentration of blood serum or blood plasma in a diluted sample. Inmany cases, blood serum or blood plasma is the test medium of choice inthe lab. Two operations may be necessary prior to running an assay,dilution and red blood cell removal. Blood samples vary significantly inthe proportion of the sample volume occupied by red cells (thehematocrit which varies from about 20-60%). Furthermore, in apoint-of-care environment when assay systems are operated by non-expertpersonnel, e.g., a device deployed in the home of an individual beingmonitored by the Health Shield, the volume of sample obtained may not bethat which is intended. If a change in volume is not recognized, it canlead to error in the reported analyte concentrations.

In related but separate embodiment, the present invention uses a methodof retrieving plasma from a blood sample comprising mixing a bloodsample in the presence of magnetizable particles in a sample collectionunit, wherein the magnetizable particles comprise an antibody capturesurface for binding to non-plasma portions of the blood sample, andapplying a magnetic field above a plasma collection area to the mixedblood sample to effect suspension of the non-plasma portions of theblood sample on top of the plasma collection area, thereby retrievingthe plasma from a blood sample.

In order to process blood samples, the device or system of the inventionmay include a magnetic reagent or object which binds to red cells andenables magnetic removal of red cells from plasma. The reagent can beprovided in lyophilized form but also can be present as a liquiddispersion. A reagent comprised of magnetizable particles (for example,about 1 micrometer in size) can be coated with an antibody to a red cellantigen or to some adaptor molecule. In some embodiments, the reagentalso contains unbound antibodies to red cell surface antigens, which maybe unlabeled or labeled with an adaptor moiety (such as biotin,digoxigenin, or fluorescein). In an embodiment analyzing a blood sample,the red blood cells in a diluted sample co-agglutinate with themagnetizable particles aided by a solution phase antibody.Alternatively, a lectin that recognizes a red cell surface carbohydratecan be used as a co-agglutination agent. Sometimes, combinations of redcell agglutinating agents are used. Alternatively, a device of theinvention can comprise a blood filter, such as a pad of glass fiber, toaid in the separation of red blood cells from a sample.

When blood is mixed with a magnetic reagent, a co-agglutination canoccur in which many, if not all, of the red cells form a mixedagglutinate with the magnetizable particles. The reagent dissolution andmixing process is driven by repeated aspiration using a tip orcollection tip of the invention or a pipette-like tip. After themagnetizable mass has formed, the mass can be separated from the bloodplasma by use of a magnet to hold the mass in place as plasma is allowedto exit the tip. In an embodiment, the plasma exits the tip by gravityin a vertical orientation, while the magnet holds the mass in place. Inanother embodiment, the plasma exits the tip by vacuum or pressuremeans, while the mass is held within the tip. The plasma can bedeposited into a well, another collection tip, or assay unit asdescribed herein.

An example of a plasma separation method of the invention isdemonstrated in FIGS. 14A through 14E. In FIG. 14A, a whole blood sample901 has been aspirated into a sample tip 910 as described herein, forexample in the amount of about 20 microliters. The whole blood sample901 is then deposited into a separation well 920 (for example, a wellcontaining magnetic beads or particles) of an example device. FIG. 14Billustrates a method of suspending and mixing a magnetic reagent in thewhole blood sample 902 in a separation well (for example, magnetic beadparticles and free binding molecules). FIG. 14C demonstrates a 10microliter air slug 930 that can be used to prevent loss from the tip910. The mixed whole blood sample and magnetic reagent 902 are incubatedfor several seconds (for example, 60 to 180 seconds) to allow anagglutination reaction to occur.

FIG. 14D demonstrates the application of a magnetic field 940 to thewhole blood cell and magnetic reagent mixture 902. The magnetic field940 can be applied by a magnetic collar 942 that is incorporated with asystem or with any magnetic means known in the art. The magnetic field940 attracts any particles that have adhered to the magnetic reagent. Inthis way, the plasma 903, which does not adhere with the magneticreagent, can be separated from non-plasma portions of a whole bloodsample.

FIG. 14E demonstrates a method of distributing a blood plasma sample903, as separated by the magnetic reagent described herein, into a wellor unit 950 of a device as described herein. The blood plasma sample 903can also be distributed to a collection tip or assay unit, as well asany other sort of assay device as obvious to one skilled in the art. InFIG. 14E, the magnetic field 940 is shown to move with the tip 910distributing the blood plasma sample 903. In this example, 5 to 8microliters of plasma have been removed from a 20 microliter whole bloodsample. 1 to 99% of a whole blood sample can be plasma separated using amethod described herein. In an embodiment, 25 to 60% of the volume ofthe whole blood sample is plasma that can be separated.

Other exemplary steps of a method as described can be completed. Inorder to move the blood plasma sample to another well or unit, acapillary plasma collection tip (which can be operated by a roboticsystem or any other system of the invention) collects the blood plasmasample by capillary and aspiration force. Another step can comprisedistributing the plasma sample in a diluent, and the sample can then bediluted by the diluent. The diluted blood plasma sample can then becollected by the collection tip in a predetermined volume. The dilutedblood plasma sample can then be mixed and distributed into a well orunit of a device to be distributed to one or a plurality of assay unitsof a device of the invention. The sample can also be distributed intoany other type of device, such as a microtiter plate, as would beobvious to those skilled in the art.

The example process demonstrated in FIGS. 14A through 14E can be usedwith other devices and systems, such as any of the FS devices describedherein. For example, a fluid transfer tip can contain the agglutinatedmass and the plasma could be deposited into a microtiter plate. Otherdevices and systems as would be obvious to those skilled in the artcould be utilized to execute the example blood plasma separation asdisclosed herein.

The sample of bodily fluid can also be diluted in a variety of othermanners, such as using a sample collection device capable of dilution.The housing of the sample collection device can comprise a tube. In thetube, two moveable seals can contain a volume of a diluent. In apreferable embodiment, the volume of the diluent is predetermined, e.g.,in about the range of 50 microliters to 1 milliliter, preferably in therange of about 100 microliters to 500 microliters.

In one embodiment, the FS devices of the invention are used in a methodfor automated detection of a plurality of analytes in a bodily fluidsample comprising: providing the bodily fluid sample to a fluidicdevice, wherein the fluidic device comprises: a sample collection unitconfigured to contain the bodily fluid sample; an array of assay units,wherein an individual assay unit of said array of assay units isconfigured to run a chemical reaction that yields a signal indicative ofan individual analyte of said plurality of analytes being detected; andan array of reagent units, wherein an individual reagent unit of saidarray of reagent units contains a reagent. The method can also compriseengaging the individual assay unit using a fluid transfer device.Continuing the method, the bodily fluid sample can be transferred fromthe sample collection unit to the individual assay unit using the fluidtransfer device and the reagent from the individual reagent unit can betransferred to the individual assay unit, thereby reacting the reagentwith the bodily fluid sample to yield the signal indicative of theindividual analyte of the plurality of analytes being detected. In someembodiments, the fluid transfer device comprises a plurality of heads,wherein an individual head of the plurality of heads is configured toengage the individual assay unit; and wherein said fluid transfer devicecomprises a programmable processor configured to direct fluid transferof the bodily fluid sample from the sample collection unit and thereagent from the individual reagent unit into the individual assay unit.

In some instances, instructions are provided to the programmableprocessor, for example, by a user, an individual, or the manufacturer.Instructions can be provided from an external device, such as a personalelectronic device or, preferably, from the OS component of the HealthShield system. The instructions can direct the step of transferring thebodily fluid sample to the individual assay unit. For example, the stepof transferring the bodily fluid sample can affect a degree of dilutionof the bodily fluid sample in the individual assay unit to bring thesignal indicative the individual analyte of the plurality of analytesbeing detected within a detectable range. In some examples, the degreeof dilution of the bodily fluid sample brings the signals indicative ofthe at least two individual analytes within a detectable range asdescribed herein. Pattern recognition techniques can be used todetermine if the detection of an analyte or a plurality of analytes by amethod as described herein is within or outside a certain range. Forexample, detectable signals outside the reportable range can berejected. The certain range can be established during calibration of afluidic device the reagent and assay units. For example, the range isestablished when a device is assembled in a just-in-time fashion.

In some instances, if the detectable signal of an analyte as detectedwith a lower dilution factor or degree of dilution exceeds that for ahigher dilution factor, the lower dilution result can be identified asinsufficient for computing a quantitative result. In most instances,concentrations of an analyte in a sample as derived from signals fromsamples with different degrees of dilution get lower as the degree ofdilution becomes greater. If this does happen, an assay result can beverified. The FS devices described herein provide the flexibility ofquality control rules such as those described that many POC devicescannot offer. The FS devices described provide many of the qualitycontrol features as would be expected in a laboratory setting.

In an embodiment, a sample is diluted in a ratio that is satisfactoryfor both high sensitivity and low sensitivity assays. For example, adilution ratio of sample to diluent can be in the range of about1:10,000-1:1. The device can enable a sample to be diluted into separatelocations or extents. The device can also enable the sample to besubject to serial dilutions. Combining the use of serial dilution withthe wide dynamic range of detection of luminescence with a PMT providesfor quantitation of analytes in a range of about 1000,000,000 fold. Forexample, for protein biomarkers the range can be from about 1 pg/mL to1000 ug/mL.

In embodiments, a sample containing an analyte for detection can bemoved from a first location to a second location by aspiration-,syringe-, or pipette-type action. The sample can be drawn into thereaction tip by capillary action or reduced atmospheric pressure. Insome embodiments, the sample is moved to many locations, including anarray of assay units of a device of the invention and different wells inthe housing of a device of the invention. The process of moving thesample can be automated by a system of the invention, as describedherein.

The assay units and/or collection tips containing the sample can also bemoved from a first location to a second location. The process of movingan assay unit or a collection tip can be automated and carried out by auser-defined protocol.

In an embodiment, the assay units are moved to collect reagent from areagent unit of the invention. In many embodiments, movement of an assayunit is automated. Aspiration-, syringe-, or pipette-type action can beused to collect reagent from a reagent unit into an assay unit.

Once a sample has been added to an assay unit that comprises a capturesurface, the entire unit can be incubated for a period of time to allowfor a reaction between the sample and the capture surface of the assayunit. The amount of time needed to incubate the reaction is oftendependent on the type of assay being run. The process can be automatedby a system of the invention. In an embodiment, the incubation time isbetween 30 seconds and 60 minutes. In another embodiment, the incubationtime is 10 minutes.

An assay unit can also be incubated at an elevated temperature. In anembodiment, the assay unit is incubated at temperature in a range ofabout 20 to 70 degrees Celsius. The assay unit can be inserted into aheating block to elevate the temperature of the assay unit and/or thecontents of the assay unit.

In an embodiment of a FS method of the invention, a conjugate is addedto the assay unit after a sample has been added to the unit. Theconjugate can contain a molecule for labeling an analyte captured by acapture surface in the assay unit. Examples of conjugates and capturesurface are described hereinafter. The conjugate can be a reagentcontained within a reagent unit. The conjugate can be distributed to theassay unit by aspiration-, syringe-, or pipette-type action. Once aconjugate has been distributed to an assay unit, the assay unit can beincubated to allow the conjugate to react with an analyte within theassay unit. The incubation time can be determined by the type of assayor the analyte to be detected. The incubation temperature can be anytemperature appropriate for the reaction.

In another embodiment, a method of calibrating a device for automateddetection of an analyte in a bodily fluid sample is used with the FSdevice of the invention. A device can comprise an array of addressableassay units configured to run a chemical reaction that yields adetectable signal indicative of the presence or absence of the analyte,and an array of addressable reagent units, each of which is addressed tocorrespond to one or more addressable assay units in said device, suchthat individual reagent units are calibrated in reference to thecorresponding assay unit(s) incorporated into a complete assay device.The final multiplexed device can then be assembled using the calibratedcomponents, making the device, and a method and system that utilize thedevice, modular components. In some embodiments, calibration formultiplexed assays is performed as above using all the assayssimultaneously in a multiplexed assay device.

Calibration can be pre-established by measuring the performance of assayreagents, such as conjugates, before the assay units and reagent unitare assembled in a device of the invention. Calibration information andalgorithms can be stored on a server linked wirelessly to the assaysystem. Calibration can be performed in advance or retrospectively byassays performed in replicate systems at a separate location or by usinginformation obtained when the assay system is used.

In an aspect, a control material can be used in a device or system tomeasure or verify the extent of dilution of a bodily fluid sample. Forexample, another issue of solid-phase based assays such as ELISA is thatan assay uses a solid-phase reagent that is difficult to quality controlwithout destruction of its function. The systems and methods hereinprovide methods to determine the dilution achieved in a POC system usinga disposable device with automated mixing and/or dilution.

In an embodiment, a method provides retrospective analysis, for example,by use of the OS component to analyze data in real time prior toreporting results. For example, an assay can be performed and a controlassay can be run in parallel to the assay. The control assay provides ameasurement of an expected dilution of the sample. In some examples, thecontrol assay can verify the dilution of the sample and thus, dilutionof a sample for the assay or plurality of assays run within the systemcan be considered accurate.

A method of measuring a volume of a liquid sample can comprise: reactinga known quantity of a control analyte in a liquid sample with a reagentto yield a detectable signal indicative of the control analyte; andcomparing an intensity of said detectable signal with an expectedintensity of said detectable signal, wherein the expected intensity ofsaid signal is indicative of an expected volume of the liquid sample,and wherein said comparison provides a measurement of said volume ofsaid liquid sample being measured. In many instances, the controlanalyte is not present in said liquid sample in a detectable amount.

In an embodiment, a method can further comprise verifying the volume ofsaid liquid sample when the measurement of the volume of the sample iswithin about 50% of the expected volume of the liquid sample.

For example, a method utilized an FS device described herein can furthercomprise: reacting a bodily fluid sample containing a target analytewith a reagent to yield a detectable signal indicative of the targetanalyte; and measuring the quantity of the target analyte in the bodilyfluid sample using an intensity of said detectable signal indicative ofthe target analyte and the measurement of said volume of said liquidsample. The liquid sample and the bodily fluid sample can be the samesample. In some embodiments, the control analyte does not react with thetarget analyte in the bodily fluid sample, therefore providing notinteracting with detection of the target analyte.

In some instances, the liquid sample (to be used as a control) and thebodily fluid sample are different liquid samples containing the analyteof interest. For example, a control liquid, such as a control solutioncontaining a known control analyte level. This type of control verifiesthat the assay chemistry is operating properly.

A control analyte used to verify correct dilution of a sample can be,without limitation, fluorescein-labeled albumin, fluorescein labeledIgG, anti-fluorescein, anti-digoxigenin, digoxigenin-labeled albumin,digoxigenin-labeled IgG, biotinylated proteins, non-human IgG. Otherexemplary control analytes can be obvious to one skilled in the art. Inan embodiment, the control analyte does not occur in a human bodilyfluid sample. In some embodiments, the control analyte is added as aliquid or in dried form to the sample.

In a POC system as described herein configured to detect a plurality ofanalytes within a sample, the system can have capabilities to dilute andmix liquids. In many instances, an automated system or user can use acontrol assay to measure the dilution actually achieved and factor thatdilution into the system calibration. For example, a control analyte canbe never found in the sample of interest and dried into a reagent unit.The quantity of the dried control analyte can be known and mixed with asample in the reagent unit. The concentration of analyte can be measuredto indicate the volume of sample and any dilution performed on thesample.

Examples of control analytes for an immunoassay include, but are notlimited to: fluorescein-labeled protein, biotinylated protein,fluorescein-labeled, Axlexa™-labeled, Rhodamine-labeled, TexasRed-labeled, immunoglobulin. For example the labeling can be achieved byhaving at least two haptens linked per molecule of protein. In someembodiments, 1-20 haptens are linked per molecule of protein. In afurther embodiment, 4-10 haptens are linked per molecule of protein.Many proteins have large numbers of free amino groups to which thehaptens can be attached. In many instances, hapten-modified proteins arestable and soluble. Also, haptens such as fluorescein and Texas Red aresufficiently large and rigid that antibodies with high affinity can bemade (for example, a hapten is large enough to fill the antibody bindingsite). In some embodiments, haptens can be attached to proteins usingreagents, such as fluorescein isothocyanate, and fluorescein carboxylicacid NHS ester to create control analytes in which the part recognizedby the assay system is the hapten.

In some embodiments, a method utilizes dried control analyte. In someexamples, a dried control analyte avoids dilution of the sample and canmake the control analyte more stable. Dried control analyte can beformulated so it dissolves rapidly and/or completely on exposure to aliquid sample. In some embodiments, a control analyte can be an analytefor which antibodies with high affinity. In some instances, a controlanalyte can be an analyte that has no cross reaction with any endogenoussample component. Additionally, for example, the analyte can beinexpensive and/or easy to make. In some embodiments, the controlanalyte is stable over the lifetime of the device or system describedherein. Exemplary carriers used to create analytes with covalentlylinked haptens include proteins such as, but not limited to: albumin,IgG, and casein. Exemplary polymer carriers used to create novelanalytes with covalently linked haptens include, but are not limited to:Dextran, Poly-vinylpyrolidone. Exemplary excipients used to formulateand stabilize control analytes include, but are not limited to: sucrose,salts, and buffers (such as sodium phosphate and tris chloride).

A control analyte and method as described herein can be used in avariety of ways including the examples described herein. For example, amethod can measure a volume of a sample. In some embodiments, a methodmeasures dilution or a dilution factor or a degree of dilution of asample. In some instances, a method provides a concentration of thecontrol analyte in a sample. In a system or device described herein todetect a plurality of analytes, measurements from a method herein usinga control analyte can be used to verify or describe measurements oftarget analytes. For example, a fluid transfer device with multipleheads may be used to distribute liquid into a plurality of assay units,including a control unit. In some instances, it can be assumed thatliquid amount distributed into the plurality of units is the same orsimilar between the individual units. In some embodiments, a methoddescribed herein with a control analyte can be used to verify that thecorrect volume of sample has been collected or utilized within a deviceor system. In another embodiment, a method verifies the correct volumeof diluent has been provided to the sample. Also, the dilution factor ordegree of dilution can also be verified. In yet another embodiment, amethod with a control analyte verifies the correct volume of dilutedsample has been distributed to the plurality of units.

FIG. 15 demonstrates an exemplary method of a control assay as describedherein comprising a known quantity of control analyte. A unit 1010before assembly into a cartridge can be filled with a solution 1001comprising a known mass of control analyte 1002. The liquid of thesolution can be dried to leave the control analyte 1002 in the unit1010. The unit 1010 can then be inserted into a device and transportedfor use. When the unit 1010 is used and receives a sample or diluent1003, the sample 1003 can be delivered in an expected volume and mixedwith the dried control analyte 1002 within the unit 1010 to create acontrol solution 1004 with an expected concentration. The controlsolution 1004 can be optionally diluted. In an embodiment, the controlanalyte 1002 can be detected in the same manner as a target analyte inthe device. The control analyte concentration in the control solution1004 is measured. The measurement of the concentration can be used tocalculate the volume of the sample 1003 added to create the controlsolution 1004. In this manner, a user can compare the measured volume ofthe sample 1003 with the expected volume of the sample 1003.

In an example, red blood cells can be removed from a blood sample.However, if some red blood cells remain, or red blood cells are notremoved from a blood sample, a method with a control analyte can be usedto correct for effects from red blood cells in the blood sample. Becausehematocrit can vary significantly (for example, from 20-60% of the totalvolume of a sample), the quantity of an analyte in a fixed or expectedvolume (v) of blood can be a function of the hematocrit (H expressedhere as a decimal fraction). For example, the quantity of analyte with aconcentration C in plasma is C*v*(1−H). Thus the quantity for a samplewith hematocrit 0.3 is 1.4 times that for a sample with hematocrit 0.5.In an exemplary embodiment, undiluted blood can be dispensed into adevice as described and red cells can be removed. A control analyteconcentration in the plasma fraction can then be measured to estimatethe volume of sample plasma and determine the hematocrit.

In some embodiments, unbound conjugate may need to be washed from areaction site to prevent unbound conjugates from producing inaccuratedetection. The limiting step of many immunoassays is a washing step. Thecompromise of minimum carryover and high sensitivity is dependent on thewash removal of unbound conjugate. The wash step can be severely limitedin a microtiter plate format due to the difficulty of removing the washliquid from a well (for example, by automatic means). An assay unitdevice can have a number of advantages in the way liquids are handled.An advantage may be an improvement in the signal to noise ratio of anassay.

Removal of the conjugate can be difficult to if conjugates are stickingto the edges of the assay units of a device if, for example, there isnot an excess of a wash solution. A wash of the conjugate can occur byeither pushing the wash solution from above or drawing the wash solutionup and expelling the liquid similar to the loading of the sample. Thewashing can be repeated as many times as necessary.

When using a wash buffer in an assay, the device can store the washbuffer in reagent units and the assay unit can be brought into fluidcommunication with the wash. In an embodiment, the wash reagent is ableto remove unbound reagent from the assay units by about 99, 99.9, or99.999% by washing. In general, a high washing efficiency resulting in ahigh degree of reduction of undesired background signals is preferred.Washing efficiency is typically defined by the ratio of signal from agiven assay to the total amount of signal generated by an assay with nowash step and can be readily determined by routine experimentation. Itcan be generally preferred to increase the volume of washing solutionand time of incubation but without sacrificing the signals from a givenassay. In some embodiments, washing is performed with about 50 ul toabout 5000 ul of washing buffer, preferably between about 50 ul to about500 ul washing buffer, for about 10 to about 300 seconds.

Additionally, it can be advantageous to use several cycles of smallvolumes of wash solution which are separated by periods of time where nowash solution is used. This sequence allows for diffusive washing, wherelabeled antibodies diffuse over time into the bulk wash solution fromprotected parts of the assay unit such as the edges or surfaces where itis loosely bound and can then be removed when the wash solution is movedfrom the reaction site.

In many embodiments, the last step is to distribute an enzymaticsubstrate to detect the conjugate by optical or electrical means.Examples of substrates are described hereinafter.

For example, the reagent in the individual reagent unit of a deviceherein can be an enzyme substrate for an immunoassay. In anotherembodiment, the step of transferring the substrate reagent from theindividual reagent unit can be repeated after a reaction at the capturesite. For example, enzymatic substrate is transferred to a reaction siteand incubated. After measuring the assay signal produced, used substratecan be removed and replaced with fresh substrate and the assay signalremeasured. A signal indicative of the individual analyte being can bedetected using a system as described herein from both the first and thesecond application of substrate. The second substrate is usually thesame as the original substrate. In an embodiment, the second substrateis transferred to a reaction site from a second reagent unit of a deviceherein. In another embodiment, the second substrate is transferred to areaction site from the same reagent unit as the original substrate.Transferring a second substrate thereby creates a second reaction toyield a second signal indicative of the individual analyte. Theintensity of the original signal and a second intensity of the secondsignal can be compared to calculate the final intensity of the signalindicative of the individual analyte and whether the assay was properlyconducted.

In an embodiment, the intensities of the multiple signals can be usedfor quality control of an assay. For example, if the signals differ by20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% or more, the assay resultsmay be disregarded.

In an embodiment, a method as described herein comprises re-loadingsample and or detector-conjugate (enzyme-labeled antibody) and or theenzyme substrate and sample to rectify or confirm an assay signal or touse as an internal control. For example, re-use of an assay tip or unitas described can be provided to verify function and/or to add furthersample or control materials obtain a second signal.

In some instances, a method of re-loading a substrate to an enzyme unitis enabled by the ability of a system as described herein toautomatically to transfer liquid samples and reagents into the assayunits. Some assays do not require the system to deliver a resultimmediately or on a schedule, therefore, a control method as describedoffers an opportunity to possibly enhance the reliability of theresults. A response observed following iterations of adding an enzymesubstrate can be used to verify the initial response or to calculatespike recovery.

Experiments have shown that by adding a second aliquot of enzymesubstrate to an assay unit, the reproducibility of results can bemaintained. In some embodiments, a control method provides replicateanalyses using an assay unit gave a response significantly lower thanthat expected.

With any control methods described herein, there are numerous possibleerrors that can be accounted for or postulated from executing a controlmethod. Exemplary assay errors include, but are not limited to, impropermanufacturing of an assay unit or device, improper aspiration of asample and/or one or more reagents, an assay unit is not positionedproperly relative to the photomultiplier during detection, and a missingassay unit in the device or system.

In some embodiments a method of automatically monitoring an individual'scompliance with a medical treatment using the subject devices or systemsis provided using the FS devices. The method comprises the steps ofallowing a sample of bodily fluid to react with assay reagents in adevice to yield a detectable signal indicative of the presence of ananalyte in said sample; detecting said signal with said device;comparing said signal with a known profile associated with said medicaltreatment to determine if the individual is compliant or noncompliantwith said medical treatment; and notifying the individual or associatedindividuals, e.g., local health care agents, of said compliance ornoncompliance. This can be important for the HS systems of the inventionbecause the mitigation policies will not be as effective if therecommended treatments are not followed. In some embodiments,noncompliance events are reported to the OS systems. The model can beupdated to account for noncompliance. The officials monitoring the OSmodeling results can also contact local officials to take action.

In another embodiment, the system and methods of the invention canidentify trends in biomarker levels and daily patient diary informationover time that can be used to adjust a drug dose to an optimal level forparticular patients (for example, adaptive dose-ranging).

In some embodiments noncompliance may include taking an improper dose ofa pharmaceutical agent including without limitation multiple doses or nodose, or may include inappropriately mixing pharmaceutical agents. Inpreferred embodiments a patient is notified substantially immediatelyafter the signal is compared with a known profile.

An individual monitored by the Health Shield may forget to take a bodilyfluid sample for analysis as described herein. In some embodiments amethod of alerting an individual to test a sample of bodily fluid usinga device as described herein comprises providing a protocol to be run onsaid device, said protocol communicated from the OS component,associated with said individual, and comprising a time and date to testsaid sample of bodily fluid; and notifying individual to test saidbodily fluid on said date and time if said sample has not been tested.In some embodiments an individual can be notified as described herein,e.g., over a wireless connection. Compliance with therapeutic regimescan be improved by use of prompts on a display and obtaining responsesfrom patients (for example, by way of a touch-screen).

In one embodiment, the system includes a convenient way to package theFS elements required for multiple complex assays in a secure form forshipping. For example, assay elements click fit into a housing.

(c) Field System Assays

A variety of assays may be performed on a fluidic device describedherein to detect an analyte of interest in a sample. A wide diversity oflabels is available in the art that can be employed for conducting thesubject assays. In some embodiments labels are detectable byspectroscopic, photochemical, biochemical, electrochemical,immunochemical, or other chemical means. For example, useful nucleicacid labels include the radioisotopes 32P, 35S, C14, H3, I125, and I131,fluorescent dyes, electron-dense reagents, and enzymes. A wide varietyof labels suitable for labeling biological components are known and arereported extensively in both the scientific and patent literature, andare generally applicable to the present invention for the labeling ofbiological components. Suitable labels include radionucleotides,enzymes, substrates, cofactors, inhibitors, fluorescent moieties,chemiluminescent moieties, bioluminescent labels, colorimetric labels orredox labels. Reagents defining assay specificity optionally include,for example, monoclonal antibodies, polyclonal antibodies, proteins,nucleic acid probes or other polymers such as affinity matrices,carbohydrates or lipids. Detection can proceed by any of a variety ofknown methods, including spectrophotometric or optical tracking ofradioactive, fluorescent, or luminescent markers, or other methods whichtrack a molecule based upon size, charge or affinity. A detectablemoiety can be of any material having a detectable physical or chemicalproperty. Such detectable labels have been well-developed in the fieldof gel electrophoresis, column chromatography, solid substrates,spectroscopic techniques, and the like, and in general, labels useful insuch methods can be applied to the present invention. Thus, a labelincludes without limitation any composition detectable by spectroscopic,photochemical, biochemical, immunochemical, nucleic acid probe-based,electrical, optical thermal, or other chemical means.

In some embodiments the label is coupled directly or indirectly to amolecule to be detected such as a product, substrate, or enzyme,according to methods well known in the art. As indicated above, a widevariety of labels are used, with the choice of label depending on thesensitivity required, ease of conjugation of the compound, stabilityrequirements, available instrumentation, and disposal provisions.Non-radioactive labels are often attached by indirect means. Generally,a receptor specific to the analyte is linked to a signal generatingmoiety. Sometimes the analyte receptor is linked to an adaptor molecule(such as biotin or avidin) and the assay reagent set includes a bindingmoiety (such as a biotinylated reagent or avidin) that binds to theadaptor and to the analyte. The analyte binds to a specific receptor onthe reaction site. A labeled reagent can form a sandwich complex inwhich the analyte is in the center. The reagent can also compete withthe analyte for receptors on the reaction site or bind to vacantreceptors on the reaction site not occupied by analyte. The label iseither inherently detectable or bound to a signal system, such as adetectable enzyme, a fluorescent compound, a chemiluminescent compound,or a chemiluminogenic entity such as an enzyme with a luminogenicsubstrate. A number of ligands and anti-ligands can be used. Where aligand has a natural anti-ligand, it can be used in conjunction withlabeled, anti-ligands. Exemplary ligand—anti-ligands pairs includewithout limitation biotin—avidin, thyroxine—anti-t4,digoxigenin—anti-digoxin, and cortisol—anti-cortisol, Alternatively, anyhaptenic or antigenic compound can be used in combination with anantibody.

In some embodiments the label can also be conjugated directly to signalgenerating compounds, for example, by conjugation with an enzyme orfluorophore. Enzymes of interest as labels will primarily be hydrolases,particularly phosphatases, esterases and glycosidases, oroxidoreductases, particularly peroxidases. Fluorescent compounds includefluorescein and its derivatives, rhodamine and its derivatives, dansylgroups, and umbelliferone. Chemiluminescent compounds includedioxetanes, acridinium esters, luciferin, and2,3-dihydrophthalazinediones, such as luminol.

Methods of detecting labels are well known to those of skilled in theart. Thus, for example, where the label is radioactive, means fordetection include scintillation counting or photographic films as inautoradiography. Where the label is fluorescent, it may be detected byexciting the fluorochrome with light of an appropriate wavelength anddetecting the resulting fluorescence by, for example, microscopy, visualinspection, via photographic film, by the use of electronic detectorssuch as digital cameras, charge coupled devices (CCDs) orphotomultipliers and phototubes, or other detection device. Similarly,enzymatic labels are detected by providing appropriate substrates forthe enzyme and detecting the resulting reaction product. Finally, simplecolorimetric labels are often detected simply by observing the color,i.e., the absorbance, associated with the label. For example, conjugatedgold often appears pink, while various conjugated beads appear the colorof the bead.

In some embodiments the detectable signal may be provided byluminescence sources Luminescence is the term commonly used to refer tothe emission of light from a substance for any reason other than a risein its temperature. In general, atoms or molecules emit photons ofelectromagnetic energy (e.g., light) when then move from an excitedstate to a lower energy state (usually the ground state). If excitingcause is a photon, the luminescence process is referred to asphotoluminescence. If the exciting cause is an electron, theluminescence process can be referred to as electroluminescence. Morespecifically, electroluminescence results from the direct injection andremoval of electrons to form an electron-hole pair, and subsequentrecombination of the electron-hole pair to emit a photon Luminescencewhich results from a chemical reaction is usually referred to aschemiluminescence Luminescence produced by a living organism is usuallyreferred to as bioluminescence. If photoluminescence is the result of aspin-allowed transition (e.g., a single-singlet transition,triplet-triplet transition), the photoluminescence process is usuallyreferred to as fluorescence. Typically, fluorescence emissions do notpersist after the exciting cause is removed as a result of short-livedexcited states which may rapidly relax through such spin-allowedtransitions. If photoluminescence is the result of a spin-forbiddentransition (e.g., a triplet-singlet transition), the photoluminescenceprocess is usually referred to as phosphorescence. Typically,phosphorescence emissions persist long after the exciting cause isremoved as a result of long-lived excited states which may relax onlythrough such spin-forbidden transitions. A luminescent label may haveany one of the above-described properties.

Suitable chemiluminescent sources include a compound which becomeselectronically excited by a chemical reaction and may then emit lightwhich serves as the detectible signal or donates energy to a fluorescentacceptor. A diverse number of families of compounds have been found toprovide chemiluminescence under a variety or conditions. One family ofcompounds is 2,3-dihydro-1,4-phthalazinedione. A frequently usedcompound is luminol, which is a 5-amino compound. Other members of thefamily include the 5-amino-6,7,8-trimethoxy- and thedimethylamino[ca]benz analog. These compounds can be made to luminescewith alkaline hydrogen peroxide or calcium hypochlorite and base.Another family of compounds is the 2,4,5-triphenylimidazoles, withlophine as the common name for the parent product. Chemiluminescentanalogs include para-dimethylamino and -methoxy substituents.Chemiluminescence may also be obtained with oxalates, usually oxalylactive esters, for example, p-nitrophenyl and a peroxide such ashydrogen peroxide, under basic conditions. Other useful chemiluminescentcompounds that are also known include —N-alkyl acridinum esters anddioxetanes. Alternatively, luciferins may be used in conjunction withluciferase or lucigenins to provide bioluminescence.

The term analytes as used herein includes without limitation drugs,prodrugs, pharmaceutical agents, drug metabolites, biomarkers such asexpressed proteins and cell markers, antibodies, serum proteins,cholesterol and other metabolites, polysaccharides, nucleic acids,biological analytes, biomarkers, genes, proteins, or hormones, or anycombination thereof. Analytes can be combinations of polypeptides,glycoproteins, polysaccharides, lipids, and nucleic acids.

Of particular interest are biomarkers are associated with a particulardisease or with a specific disease stage. Such analytes include but arenot limited to those associated with infectious diseases, autoimmunediseases, obesity, hypertension, diabetes, neuronal and/or musculardegenerative diseases, cardiac diseases, endocrine disorders, metabolicdisorders, inflammation, cardiovascular diseases, sepsis, angiogenesis,cancers, Alzheimer's disease, athletic complications, and anycombinations thereof.

Of also interest are biomarkers that are present in varying abundance inone or more of the body tissues including heart, liver, prostate, lung,kidney, bone marrow, blood, skin, bladder, brain, muscles, nerves, andselected tissues that are affected by various disease, such as differenttypes of cancer (malignant or non-metastatic), autoimmune diseases,inflammatory or degenerative diseases.

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EXAMPLES Example 1 National Influenza Healthcare Monitoring System

In this example, the Health Shield system is customized for the nationaldisease control agency and deployed as a national health shield. Theprimary objective of the program is to customize a system forcontainment and pro-active management of diseases such as influenza thatcan cause epidemics. The system is designed to identify, track, andcontain the spread of flu outbreak and significant ‘mutant’ strains(such as those with resistance to antiviral drugs or those with morevirulence) at the earliest stages of infection, thereby improvingdisease prevention and response. Inputs to the Operating System (OS)modeling efforts are used to determine an optimal sampling andcontainment strategy for influenza.

A second objective of a subject system is to improve outcomes and reducehealthcare costs by better managing and preventing the progression ofchronic diseases, starting with diabetes. The ability to improveoutcomes and dramatically reduce healthcare costs by preventing andreversing diabetes alone may reduce annual health expenditure bybillions of dollars. HS systems deployed for influenza and diabetes canalso be customized to apply to prevention and better management of otherchronic diseases such as congestive heart failure (CHF).

The Field System components are deployed nationally, with initialdeployment focused on geographic locations and/or populations consideredto be at risk. FS systems are deployed in part as robotically automatedassays run in central laboratories. The systems have automated on-boardcontrols to improve the reliability of the results. Mobile Field Systemsare also deployed in multiple points-of-care, including hospitals,clinics, doctors' offices, and public locations such as schools,pharmacies, airports, etc. The FS components are also deployed forfamily home use in rural areas where limited health-care infrastructureexists, allowing individuals in those areas to be tested remotely and asneeded communicate with health experts wirelessly without having totravel to a clinic or hospital.

For H1N1 influenza (“swine-flu”) monitoring, the FS measures antigensand antibodies to H1N1 in blood samples and saliva. The blood and salivasamples are tested on two separate cartridges. The blood tests aremultiplexed with tests for a combination of cytokines which measure thebody's response to infection.

For H1N1, the FS cartridges are customized to run six assays and twocontrols, including assays for H1N1 antibody and antigen and fourcytokines measuring the body's response to infection. Assay multiplexesare run in less than 90 minutes or less than 30 minutes depending onspecific FS configuration. The cartridges for blood and saliva areprocessed separately or together depending on specific FS configuration.As new virus strains emerge, additional assays are added to the existingpanels. For example, the H1N1 assays are further multiplexed with assaysfor H5N1 (avian or bird flu) antibodies and antigens. High volume readersystems are provided in addition to the single sample readers. The highvolume readers are configurable to run tens of samples simultaneously.

The test results are transferred to the centralized government OperatingSystem over secure high speed networks in real-time along with otherclinically relevant patient data collected through the instrumenttouch-screens or through the OS web-portal software extractinginformation from patient records. The integrated data sets are passedthrough pattern recognition algorithms to assess an individual's diseasestatus and to check for other abnormalities. The integrated analyticalsystem has controls built in to check for and identify sources ofvariability in the data. The actions taken when variability or noise inthe data is identified are built into the alerting capability of thesystem based on customized rules set for the governmental organizationprior to deployment. The rules specify when and how to notify aclinician, patient and/or patient contacts automatically by phone, emailor similar electronic communications when an actionable event isdetected.

In implementing a containment strategy for influenza, the parameters ofthe system are set to control against false negatives. The deploymentstrategy is weighed against that uncertainty. Monte Carlo modeling isused to estimate the robustness of the strategy by quantifying theuncertainty.

Table 7 below details the configuration and pilot plan for implementingthe rollout of the influenza monitoring phase. The Tasks in the tablecan be completed in parallel to accommodate a faster timeline.

TABLE 7 Rollout of influenza monitoring phase Configuration andDeployment Tasks Motivation/Notes A. Validate assays on test samples andcalibrate to establish gold standard levels of performance A.1 GainAccess to extant archived Validate a high fidelity methodology fordetection of blood/serum samples for assay development key measures ofviral load/exposure Develop insights into extent of the systemicinflammatory response in the presence of the observed viral exposureDevelop a statistical model and insight of the inflammatory measuresinvolved in disease spread B. Establish appropriate regulatorycredentials and validations C. Establish an optimal containment strategyC.1 Develop a mathematical model and simulation system of epidemicspread C.1.1 Using extant models of disease spread estimate contractrates, connectivities, incubation periods, infectious potential (i.e.,communicability), etc C.1.2 Build a Monte Carlo simulation system forunperturbed epidemic spread C.1.3 Identify candidate sampling strategies(e.g., screening in schools or workplaces, rapid follow-up of closerelatives/friends upon presentation at the hospital or clinic, etc.) andselect the most strategic locations for deployment C.1.4 Identifycandidate containment strategies (e.g., physical quarantine, pre-emptiveantiviral treatment of close contacts, etc.) C.1.5 Work with healtheconomists to evaluate each screening strategy in the context of eachcontainment strategy C.1.6 Stress model assumptions and explorequantitative impact of these assumptions on ultimate deployment strategyD. Deploy and pilot system in a government designated test site forsystem validation D.1 Based on the library of containment As dataemerges, remodel and continuously update to approaches generated above,adapt the assess whether the containment strategy is stillsampling/containment strategy to real world optimal observations D.2Adapt sampling and containment strategy to extant logistical constraintsin each region/state D.3. Identify and evaluate cost/benefits of eachalternative adaptational strategy

Two deployment scenarios for this program are as follows:

Scenario A:

Small pilot program to deploy Health Shield with many measurements atseveral locations (100,000 assay measurements in people and/or animalsmonitored across 5-7 centers/high risk locations in a contained area).The program lasts six months. Steps:

-   -   a. Customization of the Health Shield per government        requirements    -   b. Pilot program run with 100,000 measurements and 100 readers    -   c. Training of 5-7 centers/high risk locations    -   d. Modeling and simulation to identify the most effective        containment and prevention strategy in terms of outcomes and        health costs    -   e. Modeling and simulation to identify the most effective alerts        and recommended actions to be taken based on the various test        results

Scenario B:

Equip a contained region and surrounding high risk locations forcontainment and prevention of the spread of influenza while improvingtreatment of those infected. Demonstrate that the Health Shieldeffectively contains flu outbreak and prevents the spread of a virusthrough a comprehensive program in and around the local area using alarger number of measurements and locations than required by scenario A(500,000 measurements in people and/or animals across 25-30 centers/highrisk locations). The program lasts six months. Steps:

-   -   a. Customization of the Health Shield per government        requirements    -   b. Pilot program with 500,000 measurements and 500 readers    -   c. Training of 25-30 centers in and around the contained region    -   d. Modeling and simulation to identify the most effective        containment and prevention strategy in terms of outcomes and        health costs    -   e. Modeling and simulation to identify the most effective alerts        and recommended actions to be taken based on the various test        results    -   f. Activate readers to function for disease containment for any        influenza outbreak and for the management of other chronic        diseases

An integrated software component is developed for the FS systems and OSsystems, the user interface of which is shown in FIGS. 16 and 17. Theintegrated software component consists of two applications. Oneapplication shown in FIG. 16 is primarily used at regional and localtriage centers to collect individual patient data and to make specificrecommendations for treatment based on information collected from thepatient and assay data collected by the FS. There is a central officecomponent wherein data in loaded to supply the OS model with nationaland regional data describing the current state of the epidemic. Thisdata updated periodically is used to refine the model to enhanceaccuracy of prediction. Reports are generated of the data collected andactions taken at local centers.

The application shown in FIG. 17 is used in the central or nationaloffice. This application is the user interface for running the model andproducing reports generated as outputs of the model. It is here that theuser may engage in “what if” scenarios to determine appropriate actionsand mitigations to the epidemic.

The ability to detect and proactively contain the spread of mutating flustrains provides a life-saving and economic protection capability thathas not been met using existing methodology. These benefits areespecially important in decentralized and remote locations where optimalhealthcare is not readily available. The proactive health managementstrategy for chronic diseases is estimated to reduce current healthcarecosts by a third to one half of today's spending and ensure that allindividuals obtain a consistent and uniform high level of healthcare.

Example 2 Simulation of La Gloria Outbreak With and without MitigationPolicies

FIG. 18 illustrates the real world versus simulated results from anoutbreak of influenza in La Gloria, Mexico that occurred betweenFebruary and May of 2009. La Gloria is a town of about 3,000 in Mexico'sVelacruz state. Hundreds of townspeople were diagnosed with respiratoryproblems, including positive tests for swine flu (H1N1) and the morecommon H2N3 flu variant. FIG. 18 shows a comparison of the actualoutbreak data (circles) compared to a model without HS mitigation (solidline). The model with no mitigation agrees closely with actual data. Amodel with HS surveillance and mitigation policies is shown in thedashed line. The model is determined by iterative fitting of the actualoutbreak to the model of FIG. 2 until an optimal fit is achieved. Withthe HS in place, both the severity and rapidity of the outbreak areprojected to be dramatically reduced. The projected improvement is basedon the model parameters as determined for the unmitigated outbreak andusing the model to predict the outcome assuming surveillance using theHS system and home isolation of those found to be infected.

Critical Model Parameters:

basic reproduction number R0=2.2

mean generation time in days Tg=2.0

fraction of the generation time that is latent (uninfectious) fL=⅓;

For the Unmitigated Outbreak

-   -   no surveillance was performed    -   no action was taken on the infected population.        For the Mitigation Illustrated    -   60% of the symptomatics (suspected infected) reported for        voluntary testing    -   Subjects with positive result (based on assay sensitivity of 0.8        (80%) were home quarantined.

Example 3 Preventing and Reversing Diabetes

For diabetes and its complications (e.g., renal and cardiovasculardisease), the cost-benefit relationship of the Health Shield is beingquantified both through government and private programs. The programsare designed to dramatically reduce the cost of Type 2 Diabetes Mellitus(T2DM) by preventing, delaying and reversing the progression of thedisease through individualized and remotely delivered life stylemodification therapy using the HS system.

T2DM and often-associated obesity (coined the “diobesity epidemic”) leadto frequent cardiovascular, metabolic, ocular, neurologic and renalcomplications as well as increased cardiovascular morbidity andmortality. T2DM results in a heavy economic burden on the health caresystem. In the U.S. thirteen percent of adults have diabetes, and 1.6million new cases are diagnosed each year. The total estimated cost ofdiabetes in 2007 in the USA was $174 billion and 284,000 deaths in 2007were attributed to diabetes. See American Diabetes Association, DiabetesCare 31, 596 (Mar. 1, 2008).

The United States Armed Forces Services are not immune from thediobesity epidemic. For instance, there are 140,000 diabetic patientscared for by the USAF. On average, diabetes is responsible for $6,649 inexcess expenditures per year per person with diabetes. So if just 20% ofthose with diabetes have their disease delayed or reversed, the savingscome to $186,172,000 annually. In just five years the savings isanticipated to reach $1 billion. The costs of delaying the onset ofcostly micro- and macro-vascular complications are expected to producean even larger return. Id.

There is evidence that lifestyle interventions reduce the risk ofdeveloping diabetes by up to 58%. J. Tuomilehto et al., N Engl J Med344, 1343 (May 3, 2001); W. C. Knowler et al., N Engl J Med 346, 393(Feb. 7, 2002). Large epidemiologic population studies have demonstratedthat insulin resistance and the presence of metabolic syndromeparameters identify subjects at higher risk of developing T2DM andcardiovascular and cerebral events. P. W. Wilson, R. B. D'Agostino, H.Parise, L. Sullivan, J. B. Meigs, Circulation 112, 3066 (Nov. 15, 2005);C. Lorenzo, M. Okoloise, K. Williams, M. P. Stern, S. M. Haffner,Diabetes Care 26, 3153 (November, 2003). The Cardiovascular Health Studyjust demonstrated that 9 out of 10 new cases of diabetes in subjects 65years and older are attributable to 5 lifestyle factors whoseimprovement can drastically reduce the risk of diabetes up to 89%. D.Mozaffarian et al., Arch Intern Med 169, 798 (Apr. 27, 2009). Thesefactors include physical activity, diet, smoking, alcohol use, andadiposity. In the Diabetes Prevention Program (DPP), the lifestyleintervention was estimated to delay the development of T2DM by 11 yearsand to reduce the absolute incidence of diabetes by 20%. P. Lindgren etal., Int J Technol Assess Health Care 23, 177 (Spring, 2007).

Accordingly, a promising preemptive strategy to improve national healthincludes early intervention with individuals at high risk of developingT2DM. The pre-diabetic population, as defined by impaired fastingglucose (IFG) levels and/or impaired glucose tolerance (IGT), is at agreater risk of developing T2DM than their normoglycemic counterparts.However, the rate and time of conversion are difficult to predict at thelevel of individual subjects. To build on these significantepidemiologic findings, the Health Shield provides a novel diagnosticand treatment paradigm that can focus on the individual subject usingdynamic collection and analyses of physiological measures. This approachdetects and predicts earlier a subject's risk and trajectory towards thedevelopment of T2DM and subsequent cardiovascular, metabolic, ocular,neurologic and renal events. At the same time, the Health Shielddelivers to each patient individualized tools and strategies to makenecessary life-style changes. The HS reinforces the relevant healthmessages sent to users by providing physiologically relevant informationabout the effect of these life-style changes on each individual/familybasis.

Management of subjects with T2DM is performed by a comprehensive healthcare team (HCT) including physicians, nurse practitioners, physician'sassistants, nurses, dieticians, pharmacists, and mental healthprofessionals. Additionally, individuals with diabetes assume an activerole in their care and receive a comprehensive diabetes self-managementeducation to act upon. The Health Shield aids in that education andmanagement through the flexible point of care testing (POCT) andfeedback technology.

For diabetes and its complications, 6 tests are run for each time-pointwith a run-time of less than 30 minutes. Additional cartridges areprovided for renal and cardiovascular disease, each with an additional 6tests, which are processed in 15 minutes or less to detect the risk ofonset of a cardiovascular event or renal failure and assess the need fora hospital visit. This allows for patients to be treated before theirdiseases progress to the point that they need to visit costly EmergencyRooms.

POCT is defined as a near-patient testing system and has been availablefor many years, relying on bench top and hand held devices. POCTs asdiagnostic tools and clinical decision aids are now an integral part ofhealth care delivery in ambulatory care, primary care, emergency care,and operating rooms. A compelling example is the monitoring of bloodglucose during gestational diabetes mellitus that reduces the rate ofcomplications to the mother and the baby.

The Health Shield extends POCT resources to the pre-diabetic populationby delivering, e.g.:

1. A Point of Care system which serially and conveniently assesses, inreal-time, a variety of circulating blood markers that best quantify, ina dynamic way, insulin resistance, metabolic syndrome, inflammation, andcardiovascular risk. The device is also used as an interface to theMobile Healthcare System (item 3 below).

2 A mathematical/statistical learning engine that, early-on,characterizes and quantifies the risk of a given subject to develop T2DMand associated complications. The work product of the learning enginewill be the set of biological markers that best predict the onset ofdiabetes and the model that incorporates that predictive power. Thistype of analysis is typically developed during a statistical modelbuilding exercise around competing survival curves as defined by aKaplan-Meier statistic and in the context of a Cox proportional hazardsanalysis. The learning engine described herein takes advantage of thisprobability landscape by sampling at high enough frequencies so as toestablish the most informative marker patterns in the most parsimoniousmarkers subset, and from it derives a dynamic hazard/risk space for eachindividual subject in a cohort. Complementary covariates that areaccounted for in the model include age, smoking status, alcohol use,body mass index (BMI), dietary habits, exercise levels, glucose, bloodpressure and lipid levels. As additional data are made available to themodels, the system improves the probability patterns so as to morecompletely learn about each subject cohort and adjust itselfappropriately.

3. A Mobile Healthcare System that uses the integrated data, algorithms,and models described above in concert with interactions with the subjectto assist with behavior modification and increase adherence to diet,exercise and therapy. By interfacing with a subject via either a devicetouch screen or a network-integrated mobile device such as a cell phoneor PDA, the system performs the following:

-   -   Assesses the situation and mood behind the subject inquiry    -   Obtain key indicators by asking questions    -   Transmit truly individualized and context-specific content to        the device touch-screen or to the mobile device/phones to assist        users in modifying behavior

The individualized content is determined by applying artificialintelligence techniques such as Rule-Based Inference to the subject'smeasured data from the device, as well as other provided data, theanswers to the questions posed to the subject and, if available, thegeographic location of the originating call as provided by the on-boardGPS.

By integrating and analyzing the response data, the learning engine willprovide subject-specific feedback by selecting from a library aparticular item that is relevant to the subject's mood, circumstance andlocation. Items presented include nutritional advice, exercise advice,general lifestyle advice, psychological counseling, restaurant selectionin the vicinity of the subject, as well as recommended menu items withinthat restaurant, electronic coupons for food and lifestyle products,collection of nutritional or exercise data, andreinforcement/encouragement on progress toward achieving health goals.

The use of these tools and the data sent back to the clinicians help theHCT offer each individual subject tailored early therapeutic lifestylemodifications preventing the development of T2DM and its deadlycomplications.

Example 4 Diabetes Risk Prediction Visualization and Model

In a study of 187 people not known to be diabetic, subjects weresubjected to an Oral Glucose Tolerance Test (OGTT). When performing anOGTT, the individual fasts for up to fourteen hours beforehand, and onlyingests water. At initiation of the test, the individual is given ablood sugar test to determine a baseline number. Then a sugar solutionis given orally. Blood is then retested over a time course. Fordiabetes, the important numbers will come two hours into the test. For ahypoglycemic individual, blood sugar may not drop for four to six hours.

More information is available online atdiabetes-diagnosis.suite101.com/article.cfm/the_glucose_tolerance_test#ixzz0SWaqWbQr

A series of measurements of glucose and the hormone GLP-1 were madestarting with a fasting glucose level then at several time pointsfollowing the ingestion of glucose. Measured variable included:

-   -   Active GLP and Total GLP at 5 minutes before, and 10, 20, 30,        60, 90, and 120 minutes after the consumption of glucose        solution.    -   Basic profile data: age, height, weight, gender, % body fat.    -   Creatinine concentration.    -   Genetic markers: identification of single-nucleotide        polymorphism variations (SNPs) for 12 different SNP locations.    -   Fasting and post-test glucotolerance diagnoses (Normal or        Impaired Fasting Glucose; Normal or Impaired Glucotolerance or        Diabetes Mellitus)

The glucose tolerance test shows that many subjects either have diabetesor impaired glucose tolerance (IGT). The remainder have normal glucosetolerance (NGT). The GLP-1 results together with demographic information(age, sex, height) and determinations of the 12 SNPs are evaluated byrecursive partitioning using Classification and Regression Trees (CART)and generated the recursive partitioning tree shown in FIG. 19. The treeis designed to correlate with and/or predict glucose tolerance. CART isdescribed by Breiman, Friedman, Olshen, and Stone in Classification andRegression Trees, Chapman & Hall/CRC; 1st edition (Jan. 1, 1984). Thisand similar techniques develop a model through recursively dividing thedata according to indicators that will most accurate separate the data.For instance, in this example, the problem is to classify the patient'sglucotolerance state. Among the many predictors, the variable “age” withthe test criterion of 66.5 years (i.e., is a person 67 years old orolder?) gives the split with the fewest classification errors in themodel describing the study. For each resulting sub-population in eachpartition, the next most effective split is identified. By using onlypart of the data for fitting the model and the remainder for testing,the algorithm avoids overfitting the “training” data.

The analysis revealed that in the population studied, five factorsproduced an optimal categorization of the subjects: (1) age; (2) GLP-1(active) levels determined at 120 minutes following administration ofglucose; (3) height; (4) body fat (computed from height and weight); and(5), one SNP: rs10305420.

The visualization has multiple purposes. For example, a doctor can usethe tree to explain to a patient their risk factors for diabetes. Forinstance, counting the leaf (terminal) nodes from left to right, adoctor may explain to a patient that they are currently in leaf node #4(“IGT (2/11/1)”), and that as they age, they will end up in either leafnode #1 or #2, depending on their height. For a shorter patient, thiscan indicate a very severe risk of developing diabetes, and they may beadvised to take a therapeutic intervention, such as lifestyle changesand/or therapeutic treatment.

The tree can also be used to investigate different populations at riskfor diabetes. Each of the split criteria indicates a different type ofrisk and a different mechanism for separating the larger population intosubpopulations. As a result, the effect of each splitting criterioncould be examined for a causal relationship. In addition, patients whoare misclassified as diabetic are classified as such due to significantrisk factors that could contribute to their disease. As a result, itwould be worth studying this group to determine what other factors maymitigate their risk. For long-term longitudinal study development, thetree can be used to research disease progression. By selecting patientswhose condition is still NGT or IGT, but who are at elevated risk (e.g.misclassified as IGT or DM, respectively), a researcher may follow themover time to see which members of the sub-population worsen, and whichdo not, in order to understand the effects and causes of impairedglucotolerance risk factors. Similarly, the tree can be used forcomparative analyses for sub-populations of patients.

Weights (or population counts) may be assigned for a larger sample of apopulation in order to assess risk that may vary due to differentsampling strategies. For such recursive partitioning models, risk may beassessed in different geographic regions, and SIR parameters may becalculated with such trees or with ensembles of CART (classification andregression trees) and other methods, such as kernel methods and othermethods involving similarity measures, generalized linear models,various non-parametric and parametric Bayesian methods, and more.

Example 5 Cost-Matrix Adjusted Confusion Matrices

The model of the invention can be adjusted for the cost associated withdifferent errors, based on economic cost, temporal costs, or otherfactors, in order to minimize the cost of the errors made by a model.This Example present a cost analysis using the data presented in theExample above. Results are shown in Table 8.

TABLE 8 True - OGTT Predict DM (22) IGT (104) NGT (61) DM 12 10 6 IGT 982 21 NGT 1 12 34

In the table, the predicted patient category is compared with thediagnosis based on OGTT. The table was constructed without regard tocosts of errors.

A similar matrix of predictions is present below when the model isdeveloped incorporating a weighting based on misclassification costsadvised by an expert in the field. Here, the rule states that it is morecostly to predict NGT when DM is the correct state for the patient. Therationale is that certain types of error are far worse than others, suchas the eventual cost of sending a diabetic patient home with a cleanbill of health, versus the cost of follow-up testing for a patientmisclassified as diabetic.

TABLE 9 True - OGTT Predict DM (22) IGT (104) NGT (61) DM 19 11 14 IGT 390 32 NGT 0 3 15

Examples of such weights are given below in Table 10 using the costsimposed to generate Table 9. If a diabetic patient is predicted to beNGT, a penalty of 100 is assessed, while a prediction that an IGTpatient is diabetic is assessed a much lower penalty of 10: the cost ofsecondary testing and lifestyle changes would not be as significant asthe cost of medical care for the diabetic. These costs can be changed inorder to optimize the prediction model for other contexts.

TABLE 10 Correct Predicted DM IGT NGT DM 0 10 30 IGT 50 0 20 NGT 100 750

Example 6 Predicting the Onset of Infection and Sepsis and EnablingEarlier Treatment

For infection, one focus of Health Shield programs in civilian andmilitary populations has been targeted on improving outcomes in thewounded/burned/seriously ill populations and quantifying the impact ofearlier intervention/treatment (˜36-24 hours) on survival of thosepersons.

Through more frequent sampling made possible by the small volumerequirement,), and a wirelessly integrated analytical modeling engine,Health Shield Systems can be used to anticipate the onset of sepsis upto 36 hours prior to clinical diagnosis.

In this example, hospitalized patients undergoing chemotherapy for AcuteMyeloid Leukemia are monitored for the inflammatory markers IL-6, IL-1β,and Protein-C, a protein involved in coagulation control. In patientswho become septic (N=4), a combination of events occurred that do notoccur in patients who do not progress to sepsis (N=11). The eventsinclude: 1) Temperature spike to >=38C; 2) IL-6 elevated to >5 ng/mLduring a rapid spike (occurring over an interval of <12 hours); 3)Protein-C decline to <1 ug/mL; and 4) IL-1β elevated to >100 pg/mL.Individual events are indicative of occurrence of sepsis. Il-6 peaks atgreater than about 10,000 pg/mL in all subjects who become septic (FIG.36A). Protein-C declines to a minimum of about 1.3 ug/mL in all subjectswho become septic (FIG. 36B).

However, fever spike is not predictive of sepsis. Combining information(temperature, IL-6, Protein-C and IL-1β) is effective in prediction ofsepsis.

The combination of events was: IF the Temperature>380R decline inProtein-C>30%, AND subsequently IL-6 was >5 ng/mL OR IL-1β was >100pg/mL, the patient progressed to sepsis.

Table 11 shows the time elapsing from an indication of progression tosepsis as defined above to diagnosis for those patients who progress tosepsis. The event combination provides a significant window prior todiagnosis in which therapy can be initiated.

TABLE 11 Time elapsing (Days) between Marker recognition and PatientCriterion Diagnosis 1 IL-6 2.2 1 Protein-C decline 0.8 1 Fever 0.0 4IL-6 0.2 4 Protein-C 1.1 4 IL-1β 0.9 4 Fever 0.0 12 Protein-C + Fever2.0 12 Fever 2.0 15 IL-6 0.5 15 Fever 0.1

Sepsis is a whole-body inflammatory state comprising a blood infection.Sepsis can lead to septic shock, which is fatal in about 50% of cases.Sepsis and septic shock represent a challenging problem in critical caremedicine and are a major cause of mortality in the intensive care unit.In the United States, sepsis develops in 750,000 subjects and septicshock results in about 215,000 deaths per year. The incremental cost ofbloodstream infections (BSI) has been calculated to be close to $20,000.M. Kilgore, S. Brossette, Am J Infect Control 36, S172 e1 (December,2008). Patients with intensive care unit (ICU)-acquired BSIs have asignificantly increased mean length of ICU stay (15.5 vs. 12 days) andmedian costs of hospital care ($85,137 vs. $67,879) compared withpatients without ICU-acquired BSI. Id.

Initiating therapy early reduces septic shock-related mortality. Theflexible, convenient and intelligent set of tools provided by the HSenables better and earlier care at a lower cost. A salient feature ofthe system is its ease of use and the direct and active participation ofthe individual patients and the HCT. A 25% improvement in the number oflives saved correlates with a 25% decrease in the cost of care of thosepatients who would otherwise have died. In addition to those costsavings is a decrease in the cost of care of those who survive butrequire lengthy expensive treatment which with the HS system can betreated more rapidly and thus bear less cost on the health center. Thetotal cost reduction associated with HS in managing infection isestimated to be greater than 50% or over $7.5Bn per year in the UnitedStates.

The HS can identify a predictive signature of the onset of infection andsepsis in patients. A similar signature can be used in detecting thepresence of infection and the body's response to infection in personsinfected by various strains of influenza so that treatments can likewisebe customized and made earlier.

Example 7 Influenza Surveillance: Disease Detection Assays

Viral particle detection. FIG. 20A shows detection of an H1 antigen inresponse to H1:N1 particles. The assays for H1 antigen are performed asdescribed in PCT Patent Publication WO/2009/046227, filed Oct. 2, 2008and entitled “MODULAR POINT-OF-CARE DEVICES AND USES THEREOF.” Samplescontaining known concentrations of H1N1 antigen are mixed with detectorantibody and the mixture is incubated for 30 min. in 384 well microtiterplate wells coated with capture antibody. The wells are washed byrepeated aspiration of buffer and then enzyme substrate is added. After10 min, the microtiter plate is read in an M5 luminometer. The captureantibody is a monoclonal anti-H1 antibody tethered to a substrate. Thedetector antibody is a polyclonal anti-H1 antibody labeled with APase.The analyte is a particulate preparation displaying both H1 and N1antigens. Varying amounts of analyte spiked into buffer are shown inFIG. 20 on the X-axis.

Assay for H1N1 in Nasal Sample.

A nasal sample obtained using a swab is extracted using the reagents andprotocol of a kit commercially available kit (Quickvue). A buffersolution and the nasal extract with and without added H1N1 antigen areassayed (four replicate measurements/sample) according to the protocoldescribed above with the following results:

TABLE 12 Added Signal Analyte Avg. Analyte ng/mL Counts Signal CV %Assay buffer 0 611 14 Nasal swab extract 0 324 72 Assay buffer 500 296025 Nasal swab extract 500 18595 7

The response of the assay to samples with no added antigen isessentially negative and a clear distinction between samples with addedantigen and no added antigen is observed.

In a similar example using clinical samples, two assays are run on eachmultiplexed cartridge with duplicate “tips” for each assay for H1.Results are shown in FIG. 20B. In the figure, “Tips 1, 2” gives anaverage signal (counts) for one antibody pair and “Tips 3, 4” gives acount for a different assay pair. Nasal swab samples from eightInfluenza A-negative samples and 11 2009 Flu positive (H1N1) samples areassayed using the PCR method. Good discrimination between positive andnegative samples are found for the samples presented by using the datafrom both assays using the dotted line as a discriminator (cut-off).Using this threshold, there are eight true negatives, two falsenegatives, nine true positives and no false positives. The sensitivity(TP/TP+FN) is 81% and the specificity (TN/TN+FP) is 100%. Discriminationusing either assay alone is less effective than combining results ofboth assays.

Host Antibodies.

Host antibodies against influenza particles can be detected according tothe invention. The presence of such antibodies can indicate that anindividual has a decreased likelihood of active infection leading todisease. FIG. 21 show an assay designed to detect host antibodies in anFS cartridge. In this example, the capture reagent is a surrogateantigen comprising an anti-ideotope of the antibody to be measured boundto the solid phase. The detection reagent is an anti-Human IgG antibodylabeled with alkaline phosphatase. Purified humanized monoclonalantibody (analyte) is added in known concentrations to human serum asshown on the X axis. Microtiter plate wells coated with antibody toviral antigen H1 are incubated with diluted sample (human blood, plasmaor serum) mixed with alkaline phosphatase-labeled antibody to H1 for 30min at RT. The wells are washed with buffer and exposed tochemilumiogenic alkaline phosphatase substrate for 10 min before readingthe rate of production of photons an M5 luminometer (Molecular Devices).Influenza antibodies can be measured by the same method using influenzaantigen bound to the solid phase.

In another set of experiments, host antibodies to H1N1 are detecteddirectly. Capture surfaces are coated with viral antigen. An antibodypositive serum sample is diluted 10-fold and incubated with the capturesurface for 10 min, followed by incubation with APase-labeled anti-HumanIgG for 10 min. After washing the capture surface, an enzyme substrateis added and the assay signal (photon production) is measured after 10minutes. The results are shown in FIG. 22A. As seen in the figure, thesignal increases with antigen load on the surface and reaches a plateaulevel at about 1000 ng/mL of antigen.

FIG. 22B shows the results of an assay performed as above using anantibody positive sample diluted to different extents. As seen in thefigure, the assay response is titrated to a maximum level at about a10-fold dilution. As a specificity control, measurements are performedin parallel at 0, and 500 ng/mL coating viral antigen coatingconcentration. There is essentially no response at any sample dilutionwithout antigen present.

Inflammatory Markers.

Spikes in inflammatory markers, e.g., immune markers such as cytokines,can indicate that an individual is infected with an influenza strainthat is not identified by the current antigen assays or is undergoinganother acute process requiring medical support. FIG. 23 shows theresults of an assay for human cytokine IL-6 using an FS cartridge deviceaccording to the invention. In this example, the capture reagent is amonoclonal antibody to human IL-6, and the detection reagent is apolyclonal anti-Human IL-6 antibody labeled with alkaline phosphatase.Purified IL-6 is added to human plasma initially containing essentiallyno IL-6 in varying amounts as shown on the X-axis of FIG. 23. The plasmasamples are assayed in the FS system with the results shown.

In another example, a hospitalized human subject suspected of havingswine flu is monitored with the HS system. Two different cartridge typesare used on serial nasal samples collected from the subject. Onecartridge type has three different multiplexed assays for H1N1 antigen(using different pairs of antibodies), the other type has assays for theinflammatory markers 11-6 and TNF-α. As seen in FIG. 37, the antigenlevel (as measured by the count rate of the assays) increases byseveral-fold over days 6-10 of the monitoring period. Over the same timeinterval, both cytokine levels spike, thereby indicating an acuteinflammatory process.

Example 8 Sepsis Marker Assays

Sepsis is a serious medical condition characterized by a whole-bodyinflammatory state and the presence of a known or suspected infection.Sepsis can lead to septic shock, multiple organ dysfunction syndrome,and death. Protein C is a major physiological anticoagulant. The proteinC pathway's key enzyme, activated protein C, provides physiologicantithrombotic activity and exhibits both anti-inflammatory andanti-apoptotic activities. Drotrecogin alpha (activated) is recombinantactivated protein C used in the treatment of severe sepsis and septicshock. C-reactive protein (CRP) is a protein found in the blood, thelevels of which rise in acute inflammation. CRP is used mainly as amarker of inflammation, and can be used to measure disease progress ortreatment efficacy.

FIG. 24 shows the results of monitoring sepsis over time. Reagents forprotein-C and C-reactive protein (CRP) were assembled into multiplexedField System cartridges. The assay system was used to measure theseanalytes in blood samples obtained from a human patient undergoingchemotherapy. Results are plotted below against the time from beginningtherapy. The patient was diagnosed as septic at about day 6 and givenintensive care. After making a recovery and being released from the ICU,the patient again became septic at about day 18. The decline inProtein-C preceded recognition of sepsis by about a day. The severity ofthe inflammatory response to sepsis is indicated by the massive increasein CRP.

Example 9 Diabetes Surveillance: GLP-1 and C-Peptide Assays

FIG. 25 shows an assay performed using an FS cartridge system accordingto the invention for GLP-1, a hormone involved in regulating glucosemetabolism. In this example, the capture reagent is a monoclonalantibody to GLP-1 and the detection reagent is a monoclonal anti HumanGLP-1 antibody labeled with alkaline phosphatase. The samples are GLP-1free human plasma spiked with various concentrations of GLP-1, asindicated on the Y-axis in FIG. 25.

FIG. 26 shows an assay for C-Peptide, a peptide that is made whenproinsulin is split into insulin and C-peptide. There is a 1:1 ratiobetween the amount of insulin and C-peptide created. In this example,the capture reagent is a monoclonal antibody to C-peptide and thedetection reagent is a monoclonal anti-Human C-Peptide antibody labeledwith alkaline phosphatase. The samples comprise C-peptide spiked intobuffer at various concentrations, as indicated on the X-axis in FIG. 26.

FIG. 27 illustrates a correlation using an FS cartridge system accordingto the invention for measuring C-Peptide compared to the resultsobtained by measuring C-Peptide with a reference method. In thisexample, plasma samples are analyzed using an FS cartridge system and areference method (Linco). Results from the two assays are compared andcorrelate well over the entire reportable range of the assay.

The concentrations of GLP-1 and C-Peptide change in the blood inresponse to caloric intake. FIG. 28 presents the results of a clinicalstudy of response of these analytes to a food challenge. In the study,human subjects are monitored for about a day. Three subjects consume ameal following time point 0. Blood samples are collected into collectiontubes supplemented with inhibitors of GLP-1 proteolysis at the timepoints indicated on the graph. Plasma from these samples is analyzed inthe system in multiplexed assay cartridges configured to measure GLP-1(FIG. 28A) and C-peptide (FIG. 28B) simultaneously. As shown in FIG. 28,subjects exhibit very different responses with respect to both thekinetics and magnitude of the hormonal responses for both GLP-1 andC-peptide.

Example 10 Cost Savings During Clinical Trials

The demands of a clinical trial are exceptionally challenging because ofthe tremendous cost of analysis and the strict regulatory requirements.Feedback from our clinical trial experiences, where many of thepractices are even more rigorous in actual clinical practice (e.g.,higher costs for equivalent tests), suggests great cost savings usingthe Health Shield according to the invention.

The referenced savings are accumulated over a series of steps,including:

1) Sample collection.

2) Sample shipping.

3) Sample analysis.

4) Data collection.

5) Data integration.

6) Transmission of results.

7) Follow up testing and passing through the cycle again.

Steps 1 through 4 are all performed by the HS systems, therebyeliminating many potential human error phases. Further cost reductionsare realized through reduced infrastructure. The cost of reagents onHealth Shield Systems scales with volume and as higher volumes of agiven test are produced, the cost of reagents decreases significantly.The costs presented below are based on known costs of the HS system andtypical costs of conventional testing.

Health Shield vs. Conventional Infrastructure Per Assay Per Assay CostUsing Cost Using Theranos Conventional Infrastructure Blood draw  $0Blood draw  $5 Sample prep  $0 Sample prep $10 Shipping/Storage  $0Shipping/Storage  $7 Assay Reagents $59 Assay Reagents $10 Lab Tech  $0Lab Tech $25 Data Analysis  $0 Data Analysis $10 Subject  $0 Subject $10Compensation Compensation Overhead 0% Overhead 25% Total $59 Total $96

Example 11 Data Communications

This example shows the efficiency and reliability of data communicationsof a deployed Health Shield system. As described herein, the HealthShield system of the invention comprises two components, the FieldSystems (FS) and Operating System (OS). The FS units are deployed in thefield and can communicate with the centrally located OS system usingwireless communication, among others. The communication channels canprovide two-way communications. For example, assay protocols can be sentfrom the OS to the FS instruments, and assay results sent from the FSinstruments to the OS for (1) interpretation using calibrationalgorithms and (2) routing of analyte values and further analysis todesignated persons including drug company staff, doctors, patients. Toevaluate the reliability of the communication system, FS instruments aredeployed to several locations and data transmission from FS instrumentsto an OS server were recorded. Instruments were located in fourdifferent countries and in laboratories and homes of patients. Severalhundred samples are analyzed with 100% successful communication ofresults. In some cases, the instrument does not communicate on the firsttry (overall 92% success), but communication occurrs after theinstrument tried to communicate again. Attempts continue untilcommunication is successful.

TABLE 13 Efficiency and Reliability of Data Communications Data % firstSite type and Samples transmitted Communication time Trial locationassayed GSM¹ bytes attempts Retries successful 1 Homes (N = 121 3.5E+08471 22 95.3 22) + Laboratory #1, USA 2 Laboratory #2, 38 4.6E+07 158 398.1 UK 3 Laboratory #3, 435 3.8E+09 29,274 2,449 91.6 UK 4 Laboratory#4, 79 3.5E+08 344 1 99.7 UK 5 Laboratories 32 3.7E+07 120 3 97.5 #5-7NL, IT All 705 4.5E+09 30,367 2,478 91.8 ¹Global System for MobileCommunications

Example 12 VEGFR2 Assay

In this example, a Field System cartridge device is used to perform anassay for human soluble VEGFR2. The example demonstrates a type of assaythat can be performed at the point of care for monitoring cancertherapy. One significant new class of anti-cancer drugs are inhibitorsof angiogenesis that interfere with the action of VEGF on cell surfaceVEGFR2. Assays for VEGF and its receptor VEGFR2 are therefore ofinterest. The capture surface of an assay unit is coated with capturereagent as follows. The inner surface of the assay unit made frominjection molded polystyrene is exposed to a succession of coatingreagents by aspiration and pneumatic ejection. Twenty microliters ofeach coating reagents are drawn into assay units and incubated at roomtemperature for 10 minutes. The coating reagents used in this exampleare, as used in succession, Neutravidin (20 ug/mL) inCarbonate-Bicarbonate buffer (pH 9), biotinylated “capture antibody” (amonoclonal antibody directed to VEGFR2 at 20 ug/mL) in Tris bufferedsaline, (pH 8), and a “fixative” reagent containing 3% bovine serumalbumin in Tris-buffered saline. After the succession of coatings, theassay units are dried by exposure to dry air and stored desiccated.Assay units and other reagents are assembled in a housing and used forsample analysis in the instrument of the system.

Samples for analysis are distributed to the assay unit diluted in asolution of 50 mM tris-buffer (pH 8) containing bovine serum albumin andisotonic sucrose for 20 minutes. In a reagent unit comprising aconjugate, a solution of Alkaline phosphatase (bovine intestine)-labeledmonoclonal antibody directed to VEGFR2 (binding to a distinct epitope tothe antibody of the capture surface) at 250 ng/mL in a stabilizerreagent from Biostab is provided to the assay unit for 10 minutes. Afterthe conjugate is allowed to bind with the complex of the analyte boundto the capture surface, the assay unit is washed with a solutioncontained in a reagent unit (commercially available wash buffer fromAssay Designs). The assay unit is washed 5 times. Then the assay unit ismoved to collect and mix with another reagent contained in a differentreagent, a solution of a commercially available luminogenic substratefor alkaline phosphatase (KPL Phosphaglo), and incubated for 10 minutes.The reaction of the assay in the assay unit is detected by a detectorassembly of the invention.

FIG. 29 demonstrates the VEGFR2 assay response using the method of theexample. The x axis scale is VEGFR2 concentration (pg/mL); the y scaleis relative luminescence (counts). The curve is used to calibrate themodular assay unit and reagent units.

Example 13 Analyte Detection in Plasma

Magnetizable beads are 1.3 um diameter BioMag magnetic particles fromBangs Laboratories. Beads are coated (by the manufacturer) withanti-Rabbit IgG. Beads are dispersed at 14 mg/mL in tris-bufferedsucrose (or, alternatively, tris buffered saline) containing 3% bovineserum albumin and rabbit anti-human red blood cell IgG, from CedarLaneat >=1.15 mg/mL. Aliquots (10 uL of this dispersion were dispensed intoconical tubes and lyophilized (frozen in liquid N2 and lyophilized forapproximately 24 hrs. at −70 C) prior to insertion into a slot in thecartridge housing. The rabbit antibody binds both to the red cells andto the anti-rabbit IgG-coated beads and forms a co-agglutinate of beadsand red cells.

The lyophilized magnetizable bead pellet is re-suspended by adding 20 uLof whole blood then aspirating and dispensing eight times (over 1.5 min)into a conical tube.

Blood is separated by placing the tip (in a vertical orientation) in astrong, horizontally oriented magnetic field. Typically 8 uL ofessentially red cell free plasma with no observable hemolysis isrecovered from a 20 ul blood sample (70% yield of plasma). Recovery ofanalytes (compared to plasma not exposed to the magnetic separation) isclose to 100% for Protein-C, VEGF, P1GF, Insulin, GIP and GlP-1.

Example 14 C-Reactive Protein

Serial dilution of a sample for analyses of an analyte can be carriedout in a system as described herein. C-reactive protein (CRP) is anacute-phase marker. Normal levels are in the high ng/mL to low ug/mlrange. In any acute disease process, the human liver produces CRP andlevels in blood can increase to hundreds of ug/ml. CRP has presentedissues for prior art POC analytic systems because of the wide dynamicrange of analyte to be measured (>10⁵-fold).

An FS cartridge system as described herein comprising a fluid transferdevice and a cartridge or device with arrays of assay and reagent unitsis developed. Assay tips having monoclonal anti-CRP bound to their innersurface are mounted in cartridge together with a detector-antibodysolution (alkaline-phosphatase labeled monoclonal anti-CRP (having adifferent epitope specificity than that on the tips), a wash solutionand a chemiluminogenic alkaline phosphatase (PhosphaGLO™) substrate fromKPL.

To assay CRP, the cartridges are loaded with pre-diluted solutions ofCRP used without further dilution. The cartridges are processed by a FSdevice. Successively the CRP solution (10 uL), detector antibody (12 uL)are drawn into the tips incubated for 10 min at 34° C. then discarded.The tips are washed by four aspirations of 20 uL wash solution before 15uL of substrate is aspirated into the tips. After 10 min at 37° C.,light emission is measured by the instrument for 5 s. CRP concentrationis plotted against the assay signal (photon counts) and the data isfitted to a 5-term polynomial function as shown below to generate acalibration function as shown in FIG. 30.

An experiment is executed using serial dilutions of a sample containinghighly concentrated analyte to obtain an unambiguous assay response in asystem and device as described herein. Solutions of CRP (20 uL) areloaded into cartridges and serially diluted by the instrument (todilutions of 1: 50, 250, 750 and 1500-fold respectively). The dilutedsolutions are processed as above. When the diluted CRP concentrationexceeds the upper end of the calibration range of the assay (300 ng/mL),a downward response is seen (as shown below; data from two instruments).

The response as shown in FIG. 31 can be modeled using a modification ofthe Scatchard binding isotherm (S/Smax=C/(C+C0.5). The modificationassumes that the response of the assay is linearly proportional to theconcentration of the detector antibody, as is the case in this example(data not shown). Any carry-over of CRP in the diluted sample into thenext reagent (detector antibody) will react rapidly with the reagentrendering it incapable of binding to antigen bound to the solid phaseantibody. The reduction in effective concentration is reduced inproportion to the CRP carried-over and can be accounted for with afactor (D−C*f)/D.

Therefore, S=Smax*(C/(C+C0.5))*(D−C*f)/D, wherein S is the assay signal,Smax is the maximum signal (corresponding to zero carry-over), C is theconcentration of analyte, C0.5 is the concentration for half-maximalsignal (no carry-over), D is the detector antibody concentration, and fis the fractional carryover.

Values used to fit the data, is derived by optimizing each of the fourparameters below using the technique of minimization of least squaredifferences between the data and the model fit. As can be seen in FIG.31, an excellent fit is achieved and the values of the parameters Smax,C0.5 and D (see Table 14) are close to the values that can be estimatedfrom the maximum signal reached, the observed C0.5 and the knowndetector antibody concentration. This model estimated the extent ofcarry-over as 0.034% (decimal 3.83E-04).

TABLE 14 Best fit parameters to model describing biphasic CRP assayresponse Parameter Value Units Smax 7.24E+05 Counts C0.5 5.02E+01 ng/mLD 5.72E+00 ng/mL f 3.83E−04

Data can be then be viewed according to the dilution used to achieve thefinal concentration in each assay tip, and for each dilution level theresponses fit to the same response showing that the dilutions areaccurate and precise as shown in FIG. 32.

The model as described herein can be used to compute responses for anygiven dilution and set up algorithms to ensure that the analyteconcentration is only computed from tips within the calibration range.Graphic means of representing the data are shown in FIG. 33, wherein thenormalized assay response (B/Bmax) is plotted against the log normalizedconcentration (C/C0.5) for relative dilutions: 1:1 (solid line), 5:1(dashed line), and 25:1 (dotted line). FIGS. 34 and 35 illustrate asimilar example as FIG. 33 at different normalized concentrations.Simple pattern recognition algorithms can be used to identify data forhigh concentration samples. For example, for most of the dose-response,the signal decreases with dilution. When signal for any dilution equalor exceed that of the next higher dilution, the lower dilution result isrejected. In another example, concentrations derived by using thecalibration function shown above, should correspond within some systemimprecision with the known dilutions. If the calculated concentrationfor a low dilution is lower than would correspond with those for higherdilutions, the lower dilution result can be rejected.

When the assay dose-response approaches a maximum, the slope of theconcentration (ΔC/ΔS) versus signal increases. For assays in which therelative variation in signal (ΔS/S) is essentially constant (for examplesome instances of the system as described) this translates to a biggervariation in the calculated concentration result at higherconcentrations. As provided herein, dilution or serial dilution ofsample can provide a desired concentration precision (for example <10%CV) at signal levels significantly greater (for example, >10-fold)higher than the blank (zero analyte) signal but not close to the maximumsignal (for example <0.3*Max. signal). Serial dilution enables the assaysignal to be moved into this range for any relevant sampleconcentration.

By making several estimates of the analyte concentration from differentdilutions, an average value can be obtained. An average value can alsobe achieved by making replicate measurements at a single dilution level.In some instances, a serial dilution approach as offered by the methods,systems, and device described herein can often eliminate errors due tonon-linearity of dilution due to (for example) matrix effects from thesample.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A system for modeling a progression of a diseasewithin a population, comprising: a plurality of point-of-care (POC)devices each comprising at least one programmable processor forexecuting at least one assay according to at least one assay protocolwherein the assay protocol comprises at least fluid transferinstructions to be performed by the POC devices; a central computingdevice operably in data communication with said plurality of POCdevices, wherein the POC devices are positioned at distributed locationsphysically separate from the central computing device; a static databasecomponent comprising static data related to the disease and/or thepopulation; a dynamic database component comprising dynamic data aboutthe population and individual subjects, wherein the data in the dynamicdatabase component comprises an indication of disease state ofindividuals in the population; and a computer modeling component that isconfigured to generate a model of the progression of the disease withinthe population based on the data in (1) the static database componentand (2) the dynamic database component, thereby providing modelingresults; wherein said at least one assay protocol when changed at thecentral computing device based at least on said modeling results, issent from the central computing device to said plurality of POC devicesfor execution of a changed assay protocol by the devices without havingto provide new devices to the distributed locations.
 2. The system ofclaim 1, wherein the disease is an infectious disease or a chronicdisease.
 3. The system of claim 2, wherein the disease is an infectiousdisease caused by a microorganism, a microbe, a virus, a bacterium, anarchaeum, a protozoan, a protist, a fungus or a microscopic plant. 4.The system of claim 2, wherein the disease is a chronic disease orcondition selected from the group consisting of diabetes, prediabetes,insulin resistance, metabolic disorder, obesity, and cardiovasculardisease.
 5. The system of claim 1, wherein the static database componentcomprises information about the individuals in the population comprisingone or more of age, race, sex, location, genetic factors, singlenucleotide polymorphisms (SNPs), family history, disease history ortherapeutic history.
 6. The system of claim 1, wherein the staticdatabase component comprises information about the disease, wherein theinformation about the disease comprises one or more of virulence,contagiousness, mode of transmission, treatment availability, vaccineavailability, death rate, recovery time, cost of treatment, infectivity,rate of spread, rate of mutation, and past outbreak.
 7. The system ofclaim 1, wherein the data in the dynamic database component is updatedin real-time.
 8. The system of claim 1, wherein the biomarker ismeasured in a sample of bodily fluid from the individual.
 9. The systemof claim 8, wherein the bodily fluid comprises blood, plasma, serum,sputum, urine, feces, semen, mucous, lymph, saliva, or nasal lavage. 10.The system of claim 1, wherein the point of care device performs one ormore of cartridge assays, real time PCR, rapid antigen tests, viralculture, and immunoassays.
 11. The system of claim 1, wherein the pointof care device is positioned at one or more of a school, a workplace, ashopping center, a community center, a religious institution, ahospital, a health clinic, a mobile unit, or a home.
 12. The system ofclaim 1, wherein the computer modeling component is configured topredict one or more courses of action based on the modeling results,wherein the one or more courses of action are ranked according to aranking parameter.
 13. The system of claim 12, wherein the rankingparameter comprises financial considerations, number of affectedindividuals, quality-adjusted life year (QALY), and/or quality-adjustedlife year (QALY) per economic cost unit.
 14. The system of claim 12,wherein the one or more courses of action comprise a strategy to controlthe spread of the disease.
 15. The system of claim 14, wherein thestrategy to control the spread of the disease comprises one or more ofhousehold quarantine, individual quarantine, geographic quarantine,social distancing, hospitalization, school closure, work place closure,travel restrictions, public transit closure, therapeutic treatment orintervention, prophylactic treatment or intervention, vaccination,provision of protective clothing, provision of masks, and additionalpoint-of-care testing.
 16. The system of claim 1, wherein the computermodeling component is configured to estimate a surveillance strategybased on the modeling results, and wherein the surveillance strategycomprises determining the disease status of an individual or group ofindividuals using a point of care device.
 17. The system of claim 1,wherein the model of the data comprises a plurality of states, whereinthe plurality of states comprise one or more of: susceptibleindividuals, early exposed individuals, late exposed individuals, earlyinfected individuals, late infected individuals, recovered individuals,individuals who died due to the infection and/or associatedcomplications, asymptomatic individuals, individuals given therapeutictreatment, individuals given therapeutic treatment and quarantined,individuals treated prophylactically, vaccinated individuals,individuals protected due to vaccination, early infected individuals whoare hospitalized, late infected individuals who are hospitalized,susceptible individuals who are home quarantined, early exposedindividuals who are home quarantined, late exposed individuals who arehome quarantined, early infected individuals who are home quarantined,late infected individuals who are home quarantined, asymptomaticindividuals who are home quarantined, susceptible individualsquarantined in the whole neighborhood, early exposed individualsquarantined in the whole neighborhood, late exposed individualsquarantined in the whole neighborhood, early infected individualsquarantined in the whole neighborhood, late infected individualsquarantined in the whole neighborhood, asymptomatic individualsquarantined in the whole neighborhood, amount of therapeutic drug dosesavailable, amount of antivirals and/or antibiotics available to thetarget population, home quarantined individuals that are vaccinated,home quarantined individuals that are protected due to vaccination, homequarantined individuals that recovered, susceptible individualsearmarked by mitigation policies for action, early exposed individualsearmarked by mitigation policies for action, late exposed individualsearmarked by mitigation policies for action, asymptomatic individualsearmarked by mitigation policies for action, early infected individualsearmarked by mitigation policies for action, late infected individualsearmarked by mitigation policies for action, prophylactic-treatedindividuals earmarked by mitigation policies for action, vaccinatedindividuals earmarked by mitigation policies for action, protectedindividuals earmarked by mitigation policies for action, recoveredindividuals earmarked by mitigation policies for action, susceptibleindividuals earmarked for therapeutic treatment, early exposedindividuals earmarked for therapeutic treatment, late exposedindividuals earmarked for therapeutic treatment, asymptomaticindividuals earmarked for therapeutic treatment, early infectedindividuals earmarked for therapeutic treatment, late infectedindividuals earmarked for therapeutic treatment, susceptible individualsearmarked for surveillance, early exposed individuals earmarked forsurveillance, late exposed individuals earmarked for surveillance,asymptomatic individuals earmarked for surveillance, early infectedindividuals earmarked for surveillance, late infected individualsearmarked for surveillance, prophylactic individuals earmarked forsurveillance, vaccinated individuals earmarked for surveillance,protected individuals earmarked for surveillance, susceptibleindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, early exposed individuals in whole neighborhoodquarantine earmarked by mitigation policies for action, late exposedindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, asymptomatic individuals in whole neighborhoodquarantine earmarked by mitigation policies for action, early infectedindividuals in whole neighborhood quarantine earmarked by mitigationpolicies for action, late infected individuals in whole neighborhoodquarantine earmarked by mitigation policies for action,prophylactic-treated individuals in whole neighborhood quarantineindividuals earmarked by mitigation policies for action, cumulativenumber of therapeutic doses administered, cumulative number ofantivirals and/or antibiotics administered, cumulative number of homequarantined asymptomatic individuals, cumulative number of homequarantined symptomatic individuals, cumulative number of total infectedindividuals, cumulative number of infected individuals who are notquarantined, cumulative number of infected individuals with some actiontaken, cumulative number of hospitalized individuals, and cumulativenumber of deaths.
 18. The system of claim 1 wherein each of thepoint-of-care devices comprises a robotic pipette controlled by saidprogrammable processor.
 19. The system of claim 1 wherein the centralcomputing device stores a plurality of protocols for a group ofindividuals.
 20. The system of claim 1 wherein said change comprises arevised schedule for performing assays.
 21. The system of claim 1wherein an analyte set comprising one or more analytes is chosenprospectively as a sub-set of analytes from a set comprising two or moreanalytes to be assayed by an assay, wherein said assay is selected froman assay menu that can be performed by the point-of-care devices. 22.The system of claim 1 wherein the modeling component selects a first setof assays for testing and based on testing results, selects a second setof assays to test on selected individuals, wherein assays of said secondset of assays are more sensitive than corresponding assays of said firstset.